Clean the environment.

Set locations, and the working directory …


Defining phenotypes and datasets.

Create a new analysis directory, including subdirectories.
[1] FALSE
[1] FALSE
[1] FALSE
[1] FALSE

Setting working directory and listing its contents.
[1] "/Users/slaan3/PLINK/analyses/lookups/AE_20190912_010_MDICHGANS_SWVDLAAN_IL6_MCP1/scRNAseq"
[1] "20200612.AESCRNA.scrnaseq_results.RData" "AESCRNA"                                 "scRNAseq.nb.html"                        "scRNAseq.Rmd"                           
[5] "scRNAseq.Rproj"                         

… a package-installation function …

… and load those packages.

We will create a datestamp and define the Utrecht Science Park Colour Scheme.

1 ERA-CVD ‘druggable-MI-targets’

For the ERA-CVD ‘druggable-MI-targets’ project (grantnumber: 01KL1802) we will perform two related RNA sequencing (RNAseq) experiments:

  1. conventional (‘bulk’) RNAseq using RNA extracted from carotid plaque samples, n ± 700. As of Friday, June 12, 2020 all samples have been selected and RNA has been extracted; quality control (QC) was performed and we have a dataset of 635 samples.

  2. single-cell RNAseq (scRNAseq) of at least n = 40 samples (20 females, 20 males). As of Friday, June 12, 2020 data is available of 40 samples (3 females, 15 males), we are extending sampling to get more female samples.

Plaque samples are derived from carotid endarterectomies as part of the Athero-Express Biobank Study which is an ongoing study in the UMC Utrecht.

2 Background

Using a Mendelian Randomization approach, we recently examined associations between the circulating levels of 41 cytokines and growth factors and the risk of stroke in the MEGASTROKE GWAS dataset (67,000 stroke cases and 450,000 controls) and found TARGET_A as the cytokine showing the strongest association with stroke, particularly large artery and cardioembolic stroke1. Genetically elevated MCP-1 levels were also associated with a higher risk of coronary artery disease (CAD) and myocardial infarction (MI)2. Further, in a meta-analysis of observational population-based of longitudinal cohort studies we recently showed that baseline levels of TARGET_A were associated with a higher risk of ischemic stroke over follow-up3. TARGET_A

While these data suggest a central role of TARGET_A in the pathogenesis of atherosclerosis, it remains unknown if TARGET_A levels in the blood really reflect TARGET_A activity. TARGET_A is expressed in the atherosclerotic plaque and attracts monocytes in the subendothelial space4567. Thus, TARGET_A levels in the plaque might more strongly reflect TARGET_A signaling. However, it remains unknown which cells in the plaques are interacting with the circulating monocytes.

In this project we aim to map these genes to individual cells from carotid endarterectomy patients.

3 Load data

First we will load the data:

  • scRNAseq experimental data and rename the cell types.
  • Athero-Express clinical data.

3.1 AESCRNA: single-cell RNAseq from carotid plaques

Here we load the latest dataset from our Athero-Express Single Cell RNA experiment.


scRNAseqData <- readRDS(paste0(RAWDATA, "/Seuset_40_patients/Seuset_40_patients.RDS"))
scRNAseqData
An object of class Seurat 
38835 features across 6191 samples within 2 assays 
Active assay: SCT (18283 features, 3000 variable features)
 1 other assay present: RNA
 2 dimensional reductions calculated: pca, umap
N_GENES=18283

The naming/classification is based on a combination conventional markers. We do not claim to know the exact identity of each cell, rather we refer to cells as ‘KIT+ Mast cells"-like cells. Likewise we refer to the cell clusters as ’communities’ of cells that exihibit similar properties, i.e. similar defining markers (e.g. KIT).

We will rename the cell types to human readable names.

### change names for clarity
backup.scRNAseqData = scRNAseqData
# get the old names to change to new names
UMAPPlot(scRNAseqData, label = FALSE, pt.size = 1.25, label.size = 4, group.by = "ident")


unique(scRNAseqData@active.ident)
 [1] CD14+CD68+ Macrophages II   CD3+CD4+ T Cells II         CD14+CD68+ Macrophages I    CD3+CD8+ T cells I          CD34+ Endothelial Cells II  CD3+CD8A+ T Cells II       
 [7] Mixed Cells II              Mixed Cells I               CD14+CD68+ Macrophages III  NCAM1+ Natural Killer Cells ACTA2+ Smooth Muscle Cells  CD34+ Endothelial Cells I  
[13] CD3+CD4+ T Cells III        KIT+ Mast Cells             CD79A+ B Cells I            CD3+CD4+ T Cells I          CD3+CD8 T cells III         CD79A+ B Cells II          
18 Levels: CD3+CD8 T cells III CD79A+ B Cells II KIT+ Mast Cells CD3+CD4+ T Cells III CD14+CD68+ Macrophages III CD79A+ B Cells I Mixed Cells II ... CD3+CD8+ T cells I
celltypes <- c("CD14+CD68+ Macrophages I" = "CD14+CD68+ M I", 
               "CD14+CD68+ Macrophages II" = "CD14+CD68+ M II", 
               "CD14+CD68+ Macrophages III" = "CD14+CD68+ M III",
               "CD3+CD8+ T cells I" = "CD3+CD8+ T I",
               "CD3+CD8A+ T Cells II" = "CD3+CD8A+ T II ", 
               "CD3+CD8 T cells III" = "CD3+CD8 T III", 
               "CD3+CD4+ T Cells I" = "CD3+CD4+ T I", 
               "CD3+CD4+ T Cells II" = "CD3+CD4+ T II", 
               "CD3+CD4+ T Cells III" = "CD3+CD4+ T III", 
               "CD34+ Endothelial Cells I" = "CD34+ EC I", 
               "CD34+ Endothelial Cells II" = "CD34+ EC II", 
               "Mixed Cells I" = "Mixed I", 
               "Mixed Cells II" = "Mixed II", 
               "ACTA2+ Smooth Muscle Cells" = "ACTA2+ SMC", 
               "NCAM1+ Natural Killer Cells" = "NCAM1+ NK", 
               "KIT+ Mast Cells" = "KIT+ MC",
               "CD79A+ B Cells I" = "CD79A+ B I", 
               "CD79A+ B Cells II" = "CD79A+ B II")

scRNAseqData <- Seurat::RenameIdents(object = scRNAseqData, 
                                       celltypes)
UMAPPlot(scRNAseqData, label = TRUE, pt.size = 1.25, label.size = 4, group.by = "ident",
         repel = TRUE)

3.2 Athero-Express Biobank Study: clinical data

Loading Athero-Express clinical data.

require(haven)

# AEDB <- haven::read_sav(paste0(AEDB_loc, "/2019-3NEW_AtheroExpressDatabase_ScientificAE_02072019_IC_added.sav"))
AEDB <- haven::read_sav(paste0(AEDB_loc, "/2020_1_NEW_AtheroExpressDatabase_ScientificAE_16-03-2020.sav"))

3.2.1 Fix clinical data

We need to be very strict in defining symptoms. Therefore we will fix a new variable that groups symptoms at inclusion.

Coding of symptoms is as follows:

  • missing -999
  • Asymptomatic 0
  • TIA 1
  • minor stroke 2
  • Major stroke 3
  • Amaurosis fugax 4
  • Four vessel disease 5
  • Vertebrobasilary TIA 7
  • Retinal infarction 8
  • Symptomatic, but aspecific symtoms 9
  • Contralateral symptomatic occlusion 10
  • retinal infarction 11
  • armclaudication due to occlusion subclavian artery, CEA needed for bypass 12
  • retinal infarction + TIAs 13
  • Ocular ischemic syndrome 14
  • ischemisch glaucoom 15
  • subclavian steal syndrome 16
  • TGA 17

We will group as follows:

  1. Asymptomatic > 0
  2. TIA > 1, 7, 13
  3. Stroke > 2, 3
  4. Ocular > 4, 14, 15
  5. Retinal infarction > 8, 11
  6. Other > 5, 9, 10, 12, 16, 17

# Fix symptoms

attach(AEDB)
AEDB[,"Symptoms.5G"] <- NA
AEDB$Symptoms.5G[sympt == 0] <- "Asymptomatic"
AEDB$Symptoms.5G[sympt == 1 | sympt == 7 | sympt == 13] <- "TIA"
AEDB$Symptoms.5G[sympt == 2 | sympt == 3] <- "Stroke"
AEDB$Symptoms.5G[sympt == 4 | sympt == 14 | sympt == 15 ] <- "Ocular"
AEDB$Symptoms.5G[sympt == 8 | sympt == 11] <- "Retinal infarction"
AEDB$Symptoms.5G[sympt == 5 | sympt == 9 | sympt == 10 | sympt == 12 | sympt == 16 | sympt == 17] <- "Other"


# AsymptSympt
AEDB[,"AsymptSympt"] <- NA
AEDB$AsymptSympt[sympt == -999] <- NA
AEDB$AsymptSympt[sympt == 0] <- "Asymptomatic"
AEDB$AsymptSympt[sympt == 1 | sympt == 7 | sympt == 13 | sympt == 2 | sympt == 3] <- "Symptomatic"
AEDB$AsymptSympt[sympt == 4 | sympt == 14 | sympt == 15 | sympt == 8 | sympt == 11 | sympt == 5 | sympt == 9 | sympt == 10 | sympt == 12 | sympt == 16 | sympt == 17] <- "Ocular and others"

# AsymptSympt
AEDB[,"AsymptSympt2G"] <- NA
AEDB$AsymptSympt2G[sympt == -999] <- NA
AEDB$AsymptSympt2G[sympt == 0] <- "Asymptomatic"
AEDB$AsymptSympt2G[sympt == 1 | sympt == 7 | sympt == 13 | sympt == 2 | sympt == 3 | sympt == 4 | sympt == 14 | sympt == 15 | sympt == 8 | sympt == 11 | sympt == 5 | sympt == 9 | sympt == 10 | sympt == 12 | sympt == 16 | sympt == 17] <- "Symptomatic"

detach(AEDB)

# table(AEDB$sympt, useNA = "ifany")
# table(AEDB$AsymptSympt2G, useNA = "ifany")
# table(AEDB$Symptoms.5G, useNA = "ifany")
# 
# table(AEDB$AsymptSympt2G, AEDB$sympt, useNA = "ifany")
# table(AEDB$Symptoms.5G, AEDB$sympt, useNA = "ifany")
table(AEDB$AsymptSympt2G, AEDB$Symptoms.5G, useNA = "ifany")
              
               Asymptomatic Ocular Other Retinal infarction Stroke  TIA <NA>
  Asymptomatic          333      0     0                  0      0    0    0
  Symptomatic             0    416   119                 43    732 1045    0
  <NA>                    0      0     0                  0      0    0 1103
# AEDB.temp <- subset(AEDB,  select = c("STUDY_NUMBER", "UPID", "Age", "Gender", "Hospital", "Artery_summary", "sympt", "Symptoms.5G", "AsymptSympt"))
# require(labelled)
# AEDB.temp$Gender <- to_factor(AEDB.temp$Gender)
# AEDB.temp$Hospital <- to_factor(AEDB.temp$Hospital)
# AEDB.temp$Artery_summary <- to_factor(AEDB.temp$Artery_summary)
# 
# DT::datatable(AEDB.temp[1:10,], caption = "Excerpt of the whole AEDB.", rownames = FALSE)
# 
# table(AEDB.temp$Symptoms.5G, AEDB.temp$AsymptSympt)
# 
# rm(AEDB.temp)

We will also fix the plaquephenotypes variable.

Coding of symptoms is as follows:

  • missing -999
  • not relevant -888
  • fibrous 1
  • fibroatheromatous 2
  • atheromatous 3

# Fix plaquephenotypes
attach(AEDB)
AEDB[,"OverallPlaquePhenotype"] <- NA
AEDB$OverallPlaquePhenotype[plaquephenotype == -999] <- NA
AEDB$OverallPlaquePhenotype[plaquephenotype == -999] <- NA
AEDB$OverallPlaquePhenotype[plaquephenotype == 1] <- "fibrous"
AEDB$OverallPlaquePhenotype[plaquephenotype == 2] <- "fibroatheromatous"
AEDB$OverallPlaquePhenotype[plaquephenotype == 3] <- "atheromatous"
detach(AEDB)

# AEDB.temp <- subset(AEDB,  select = c("STUDY_NUMBER", "UPID", "Age", "Gender", "Hospital", "Artery_summary", "plaquephenotype", "OverallPlaquePhenotype"))
# require(labelled)
# AEDB.temp$Gender <- to_factor(AEDB.temp$Gender)
# AEDB.temp$Hospital <- to_factor(AEDB.temp$Hospital)
# AEDB.temp$Artery_summary <- to_factor(AEDB.temp$Artery_summary)
# 
# DT::datatable(AEDB.temp[1:10,], caption = "Excerpt of the whole AEDB.", rownames = FALSE)
# 
# rm(AEDB.temp)

We will also fix the diabetes status variable.


# Fix diabetes
attach(AEDB)
AEDB[,"DiabetesStatus"] <- NA
AEDB$DiabetesStatus[DM.composite == -999] <- NA
AEDB$DiabetesStatus[DM.composite == 0] <- "Control (no Diabetes Dx/Med)"
AEDB$DiabetesStatus[DM.composite == 1] <- "Diabetes"
detach(AEDB)

# AEDB.temp <- subset(AEDB,  select = c("STUDY_NUMBER", "UPID", "Age", "Gender", "Hospital", "Artery_summary", "DM.composite", "DiabetesStatus"))
# require(labelled)
# AEDB.temp$Gender <- to_factor(AEDB.temp$Gender)
# AEDB.temp$Hospital <- to_factor(AEDB.temp$Hospital)
# AEDB.temp$Artery_summary <- to_factor(AEDB.temp$Artery_summary)
# AEDB.temp$DiabetesStatus <- to_factor(AEDB.temp$DiabetesStatus)
# 
# DT::datatable(AEDB.temp[1:10,], caption = "Excerpt of the whole AEDB.", rownames = FALSE)
# 
# rm(AEDB.temp)

We will also fix the smoking status variable. We are interested in whether someone never, ever or is currently (at the time of inclusion) smoking. This is based on the questionnaire.

  • diet801: are you a smoker?
  • diet802: did you smoke in the past?

We already have some variables indicating smoking status:

  • SmokingReported: patient has reported to smoke.
  • SmokingYearOR: smoking in the year of surgery?
  • SmokerCurrent: currently smoking?
require(labelled)
AEDB$diet801 <- to_factor(AEDB$diet801)
AEDB$diet802 <- to_factor(AEDB$diet802)
AEDB$diet805 <- to_factor(AEDB$diet805)
AEDB$SmokingReported <- to_factor(AEDB$SmokingReported)
AEDB$SmokerCurrent <- to_factor(AEDB$SmokerCurrent)
AEDB$SmokingYearOR <- to_factor(AEDB$SmokingYearOR)

# table(AEDB$diet801)
# table(AEDB$diet802)
# table(AEDB$SmokingReported)
# table(AEDB$SmokerCurrent)
# table(AEDB$SmokingYearOR)
# table(AEDB$SmokingReported, AEDB$SmokerCurrent, useNA = "ifany", dnn = c("Reported smoking", "Current smoker"))
# 
# table(AEDB$diet801, AEDB$diet802, useNA = "ifany", dnn = c("Smoker", "Past smoker"))

cat("\nFixing smoking status.\n")

Fixing smoking status.
attach(AEDB)
AEDB[,"SmokerStatus"] <- NA
AEDB$SmokerStatus[diet802 == "don't know"] <- "Never smoked"
AEDB$SmokerStatus[diet802 == "I still smoke"] <- "Current smoker"
AEDB$SmokerStatus[SmokerCurrent == "no" & diet802 == "no"] <- "Never smoked"
AEDB$SmokerStatus[SmokerCurrent == "no" & diet802 == "yes"] <- "Ex-smoker"
AEDB$SmokerStatus[SmokerCurrent == "yes"] <- "Current smoker"
AEDB$SmokerStatus[SmokerCurrent == "no data available/missing"] <- NA
# AEDB$SmokerStatus[is.na(SmokerCurrent)] <- "Never smoked"
detach(AEDB)

cat("\n* Current smoking status.\n")

* Current smoking status.
table(AEDB$SmokerCurrent,
      useNA = "ifany", 
      dnn = c("Current smoker"))
Current smoker
no data available/missing                        no                       yes                      <NA> 
                        0                      2364                      1308                       119 
cat("\n* Updated smoking status.\n")

* Updated smoking status.
table(AEDB$SmokerStatus,
      useNA = "ifany", 
      dnn = c("Updated smoking status"))
Updated smoking status
Current smoker      Ex-smoker   Never smoked           <NA> 
          1308           1814            389            280 
cat("\n* Comparing to 'SmokerCurrent'.\n")

* Comparing to 'SmokerCurrent'.
table(AEDB$SmokerStatus, AEDB$SmokerCurrent, 
      useNA = "ifany", 
      dnn = c("Updated smoking status", "Current smoker"))
                      Current smoker
Updated smoking status no data available/missing   no  yes <NA>
        Current smoker                         0    0 1308    0
        Ex-smoker                              0 1814    0    0
        Never smoked                           0  389    0    0
        <NA>                                   0  161    0  119
# AEDB.temp <- subset(AEDB,  select = c("STUDY_NUMBER", "UPID", "Age", "Gender", "Hospital", "Artery_summary", "DM.composite", "DiabetesStatus"))
# require(labelled)
# AEDB.temp$Gender <- to_factor(AEDB.temp$Gender)
# AEDB.temp$Hospital <- to_factor(AEDB.temp$Hospital)
# AEDB.temp$Artery_summary <- to_factor(AEDB.temp$Artery_summary)
# AEDB.temp$DiabetesStatus <- to_factor(AEDB.temp$DiabetesStatus)
# 
# DT::datatable(AEDB.temp[1:10,], caption = "Excerpt of the whole AEDB.", rownames = FALSE)
# 
# rm(AEDB.temp)

We will also fix the alcohol status variable.


# Fix diabetes
attach(AEDB)
AEDB[,"AlcoholUse"] <- NA
AEDB$AlcoholUse[diet810 == -999] <- NA
AEDB$AlcoholUse[diet810 == 0] <- "No"
AEDB$AlcoholUse[diet810 == 1] <- "Yes"
detach(AEDB)

# AEDB.temp <- subset(AEDB,  select = c("STUDY_NUMBER", "UPID", "Age", "Gender", "Hospital", "Artery_summary", "diet810", "AlcoholUse"))
# require(labelled)
# AEDB.temp$Gender <- to_factor(AEDB.temp$Gender)
# AEDB.temp$Hospital <- to_factor(AEDB.temp$Hospital)
# AEDB.temp$Artery_summary <- to_factor(AEDB.temp$Artery_summary)
# AEDB.temp$AlcoholUse <- to_factor(AEDB.temp$AlcoholUse)
# 
# DT::datatable(AEDB.temp[1:10,], caption = "Excerpt of the whole AEDB.", rownames = FALSE)
# 
# rm(AEDB.temp)

3.2.2 Prepare baseline characteristics

We are interested in the following variables at baseline.

  • Age (years)
  • Female sex (N, %)
  • Hypertension (N, %)
  • SBP (mmHg)
  • DBP (mmHg)
  • Diabetes mellitus (N, %)
  • Total cholesterol levels (mg/dL)
  • LDL cholesterol levels (mg/dL)
  • HDL cholesterol levels (mg/dL)
  • Triglyceride levels (mg/dL)
  • Use of statins (N, %)
  • Use of antiplatelet drugs (N, %)
  • BMI (kg/m²)
  • Smoking status (N, %)
    • Never smokers
    • Ex-smokers
    • Current smokers
  • History of CAD (N, %)
  • History of PAD (N, %)
  • Clinical manifestations
    • Asymptomatic
    • Amaurosis fugax
    • TIA
    • Stroke
  • eGFR (mL/min/1.73 m²)
  • MCP-1 plaque levels (pg/mL)
cat("====================================================================================================\n")
====================================================================================================
cat("SELECTION THE SHIZZLE\n")
SELECTION THE SHIZZLE
### Artery levels
# AEdata$Artery_summary: 
#           value                                                                                   label
# NOT USE - 0 No artery known (yet), no surgery (patient ill, died, exited study), re-numbered to AAA
# USE - 1                                                                  carotid (left & right)
# USE - 2                                               femoral/iliac (left, right or both sides)
# NOT USE - 3                                               other carotid arteries (common, external)
# NOT USE - 4                                   carotid bypass and injury (left, right or both sides)
# NOT USE - 5                                                         aneurysmata (carotid & femoral)
# NOT USE - 6                                                                                   aorta
# NOT USE - 7                                            other arteries (renal, popliteal, vertebral)
# NOT USE - 8                        femoral bypass, angioseal and injury (left, right or both sides)

### AEdata$informedconsent
#           value                                                                                           label
# NOT USE - -999                                                                                         missing
# NOT USE - 0                                                                                        no, died
# USE - 1                                                                                             yes
# USE - 2                                                             yes, health treatment when possible
# USE - 3                                                                        yes, no health treatment
# USE - 4                                                yes, no health treatment, no commercial business
# NOT USE - 5                                                          yes, no tissue, no commerical business
# NOT USE - 6                      yes, no tissue, no questionnaires, no medical info, no commercial business
# USE - 7                             yes, no questionnaires, no health treatment, no commercial business
# USE - 8                                          yes, no questionnaires, health treatment when possible
# NOT USE - 9                  yes, no tissue, no questionnaires, no health treatment, no commerical business
# USE - 10                               yes, no health treatment, no medical info, no commercial business
# NOT USE - 11 yes, no tissue, no questionnaires, no health treatment, no medical info, no commercial business
# USE - 12                                                     yes, no questionnaires, no health treatment
# NOT USE - 13                                                             yes, no tissue, no health treatment
# NOT USE - 14                                                               yes, no tissue, no questionnaires
# NOT USE - 15                                                  yes, no tissue, health treatment when possible
# NOT USE - 16                                                                                  yes, no tissue
# USE - 17                                                                     yes, no commerical business
# USE - 18                                     yes, health treatment when possible, no commercial business
# USE - 19                                                    yes, no medical info, no commercial business
# USE - 20                                                                          yes, no questionnaires
# NOT USE - 21                         yes, no tissue, no questionnaires, no health treatment, no medical info
# NOT USE - 22                  yes, no tissue, no questionnaires, no health treatment, no commercial business
# USE - 23                                                                            yes, no medical info
# USE - 24                                                  yes, no questionnaires, no commercial business
# USE - 25                                    yes, no questionnaires, no health treatment, no medical info
# USE - 26                  yes, no questionnaires, health treatment when possible, no commercial business
# USE - 27                                                      yes,  no health treatment, no medical info
# NOT USE - 28                                                                             no, doesn't want to
# NOT USE - 29                                                                              no, unable to sign
# NOT USE - 30                                                                                 no, no reaction
# NOT USE - 31                                                                                        no, lost
# NOT USE - 32                                                                                     no, too old
# NOT USE - 34                                            yes, no medical info, health treatment when possible
# NOT USE - 35                                             no (never asked for IC because there was no tissue)
# USE - 36                    yes, no medical info, no commercial business, health treatment when possible
# NOT USE - 37                                                                                    no, endpoint
# USE - 38                                                         wil niets invullen, wel alles gebruiken
# USE - 39                                           second informed concents: yes, no commercial business
# NOT USE - 40                                                                              nooit geincludeerd

cat("- sanity checking PRIOR to selection")
- sanity checking PRIOR to selection
library(data.table)
require(labelled)
ae.gender <- to_factor(AEDB$Gender)
ae.hospital <- to_factor(AEDB$Hospital)
table(ae.gender, ae.hospital, dnn = c("Sex", "Hospital"))
        Hospital
Sex      St. Antonius, Nieuwegein UMC Utrecht
  female                      524         636
  male                       1211        1420
ae.artery <- to_factor(AEDB$Artery_summary)
table(ae.artery, ae.gender, dnn = c("Sex", "Artery"))
                                                                                         Artery
Sex                                                                                       female male
  No artery known (yet), no surgery (patient ill, died, exited study), re-numbered to AAA      0    0
  carotid (left & right)                                                                     805 1781
  femoral/iliac (left, right or both sides)                                                  320  796
  other carotid arteries (common, external)                                                   17   35
  carotid bypass and injury (left, right or both sides)                                        6    3
  aneurysmata (carotid & femoral)                                                              1    0
  aorta                                                                                        3    5
  other arteries (renal, popliteal, vertebral)                                                 4    9
  femoral bypass, angioseal and injury (left, right or both sides)                             4    2
rm(ae.gender, ae.hospital, ae.artery)

# I change numeric and factors manually because, well, I wouldn't know how to fix it otherwise
# to have this 'tibble' work with 'tableone'... :-)

AEDB$Age <- as.numeric(AEDB$Age)
AEDB$diastoli <- as.numeric(AEDB$diastoli)
AEDB$systolic <- as.numeric(AEDB$systolic)

AEDB$TC_finalCU <- as.numeric(AEDB$TC_finalCU)
AEDB$LDL_finalCU <- as.numeric(AEDB$LDL_finalCU)
AEDB$HDL_finalCU <- as.numeric(AEDB$HDL_finalCU)
AEDB$TG_finalCU <- as.numeric(AEDB$TG_finalCU)

AEDB$TC_final <- as.numeric(AEDB$TC_final)
AEDB$LDL_final <- as.numeric(AEDB$LDL_final)
AEDB$HDL_final <- as.numeric(AEDB$HDL_final)
AEDB$TG_final <- as.numeric(AEDB$TG_final)

AEDB$Age <- as.numeric(AEDB$Age)
AEDB$GFR_MDRD <- as.numeric(AEDB$GFR_MDRD)
AEDB$BMI <- as.numeric(AEDB$BMI)
AEDB$eCigarettes <- as.numeric(AEDB$eCigarettes)
AEDB$ePackYearsSmoking <- as.numeric(AEDB$ePackYearsSmoking)
AEDB$EP_composite_time <- as.numeric(AEDB$EP_composite_time)

AEDB$macmean0 <- as.numeric(AEDB$macmean0)
AEDB$smcmean0 <- as.numeric(AEDB$smcmean0)
AEDB$neutrophils <- as.numeric(AEDB$neutrophils)
AEDB$Mast_cells_plaque <- as.numeric(AEDB$Mast_cells_plaque)
AEDB$vessel_density_averaged <- as.numeric(AEDB$vessel_density_averaged)

AEDB$IL6 <- as.numeric(AEDB$IL6)
AEDB$IL6_pg_ug_2015 <- as.numeric(AEDB$IL6_pg_ug_2015)
AEDB$IL6R_pg_ug_2015 <- as.numeric(AEDB$IL6R_pg_ug_2015)
AEDB$MCP1 <- as.numeric(AEDB$MCP1)
AEDB$MCP1_pg_ug_2015 <- as.numeric(AEDB$MCP1_pg_ug_2015)
AEDB$hsCRP_plasma <- as.numeric(AEDB$hsCRP_plasma)

require(labelled)
AEDB$ORyear <- to_factor(AEDB$ORyear)
AEDB$Gender <- to_factor(AEDB$Gender)
AEDB$Hospital <- to_factor(AEDB$Hospital)
AEDB$KDOQI <- to_factor(AEDB$KDOQI)
AEDB$BMI_WHO <- to_factor(AEDB$BMI_WHO)
AEDB$DiabetesStatus <- to_factor(AEDB$DiabetesStatus)
AEDB$SmokerStatus <- to_factor(AEDB$SmokerStatus)
AEDB$AlcoholUse <- to_factor(AEDB$AlcoholUse)

AEDB$Hypertension.selfreport <- to_factor(AEDB$Hypertension1)
AEDB$Hypertension.selfreportdrug <- to_factor(AEDB$Hypertension2)
AEDB$Hypertension.composite <- to_factor(AEDB$Hypertension.composite)
AEDB$Hypertension.drugs <- to_factor(AEDB$Hypertension.drugs)

AEDB$Med.anticoagulants <- to_factor(AEDB$Med.anticoagulants)
AEDB$Med.all.antiplatelet <- to_factor(AEDB$Med.all.antiplatelet)
AEDB$Med.Statin.LLD <- to_factor(AEDB$Med.Statin.LLD)

AEDB$Stroke_Dx <- to_factor(AEDB$Stroke_Dx)
AEDB$CAD_history <- to_factor(AEDB$CAD_history)
AEDB$PAOD <- to_factor(AEDB$PAOD)
AEDB$Peripheral.interv <- to_factor(AEDB$Peripheral.interv)

AEDB$sympt <- to_factor(AEDB$sympt)
AEDB$Symptoms.3g <- to_factor(AEDB$Symptoms.3g)
AEDB$Symptoms.4g <- to_factor(AEDB$Symptoms.4g)
AEDB$Symptoms.5G <- to_factor(AEDB$Symptoms.5G)
AEDB$AsymptSympt <- to_factor(AEDB$AsymptSympt)
AEDB$AsymptSympt2G <- to_factor(AEDB$AsymptSympt2G)


AEDB$restenos <- to_factor(AEDB$restenos)
AEDB$stenose <- to_factor(AEDB$stenose)
AEDB$EP_composite <- to_factor(AEDB$EP_composite)
AEDB$Macrophages.bin <- to_factor(AEDB$Macrophages.bin)
AEDB$SMC.bin <- to_factor(AEDB$SMC.bin)
AEDB$IPH.bin <- to_factor(AEDB$IPH.bin)
AEDB$Calc.bin <- to_factor(AEDB$Calc.bin)
AEDB$Collagen.bin <- to_factor(AEDB$Collagen.bin)
AEDB$Fat.bin_10 <- to_factor(AEDB$Fat.bin_10)
AEDB$Fat.bin_40 <- to_factor(AEDB$Fat.bin_40)
AEDB$OverallPlaquePhenotype <- to_factor(AEDB$OverallPlaquePhenotype)

AEDB$Artery_summary <- to_factor(AEDB$Artery_summary)

AEDB$informedconsent <- to_factor(AEDB$informedconsent)

AEDB.CEA <- subset(AEDB,
                    (Artery_summary == "carotid (left & right)" | Artery_summary == "other carotid arteries (common, external)") & # we only want carotids
                       informedconsent != "missing" & # we are really strict in selecting based on 'informed consent'!
                       informedconsent != "no, died" &
                       informedconsent != "yes, no tissue, no commerical business" &
                       informedconsent != "yes, no tissue, no questionnaires, no medical info, no commercial business" &
                       informedconsent != "yes, no tissue, no questionnaires, no health treatment, no commerical business" &
                       informedconsent != "yes, no tissue, no questionnaires, no health treatment, no medical info, no commercial business" &
                       informedconsent != "yes, no tissue, no health treatment" &
                       informedconsent != "yes, no tissue, no questionnaires" &
                       informedconsent != "yes, no tissue, health treatment when possible" &
                       informedconsent != "yes, no tissue" &
                       informedconsent != "yes, no tissue, no questionnaires, no health treatment, no medical info" &
                       informedconsent != "yes, no tissue, no questionnaires, no health treatment, no commercial business" &
                       informedconsent != "no, doesn't want to" &
                       informedconsent != "no, unable to sign" &
                       informedconsent != "no, no reaction" &
                       informedconsent != "no, lost" &
                       informedconsent != "no, too old" &
                       informedconsent != "yes, no medical info, health treatment when possible" &
                       informedconsent != "no (never asked for IC because there was no tissue)" &
                       informedconsent != "no, endpoint" &
                       informedconsent != "nooit geincludeerd" & 
                     !is.na(AsymptSympt2G))
AEDB.CEA[1:10, 1:10]
dim(AEDB.CEA)
[1] 2421 1100
cat("===========================================================================================\n")
===========================================================================================
cat("CREATE BASELINE TABLE\n")
CREATE BASELINE TABLE
# Baseline table variables
basetable_vars = c("Hospital", "ORyear",
                   "Age", "Gender", 
                   "TC_finalCU", "LDL_finalCU", "HDL_finalCU", "TG_finalCU", 
                   "TC_final", "LDL_final", "HDL_final", "TG_final", 
                   "hsCRP_plasma",
                   "systolic", "diastoli", "GFR_MDRD", "BMI", 
                   "KDOQI", "BMI_WHO",
                   "SmokerStatus", "AlcoholUse",
                   "DiabetesStatus", 
                   "Hypertension.selfreport", "Hypertension.selfreportdrug", "Hypertension.composite", "Hypertension.drugs", 
                   "Med.anticoagulants", "Med.all.antiplatelet", "Med.Statin.LLD", 
                   "Stroke_Dx", "sympt", "Symptoms.5G", "AsymptSympt", "AsymptSympt2G",
                   "restenos", "stenose",
                   "CAD_history", "PAOD", "Peripheral.interv", 
                   "EP_composite", "EP_composite_time",
                   "macmean0", "smcmean0", "Macrophages.bin", "SMC.bin",
                   "neutrophils", "Mast_cells_plaque",
                   "IPH.bin", "vessel_density_averaged",
                   "Calc.bin", "Collagen.bin", 
                   "Fat.bin_10", "Fat.bin_40", "OverallPlaquePhenotype",
                   "IL6", "IL6_pg_ug_2015", "IL6R_pg_ug_2015",
                   "MCP1", "MCP1_pg_ug_2015")

basetable_bin = c("Gender", 
                  "KDOQI", "BMI_WHO",
                  "SmokerStatus", "AlcoholUse",
                  "DiabetesStatus", 
                  "Hypertension.selfreport", "Hypertension.selfreportdrug", "Hypertension.composite", "Hypertension.drugs", 
                  "Med.anticoagulants", "Med.all.antiplatelet", "Med.Statin.LLD", 
                  "Stroke_Dx", "sympt", "Symptoms.5G", "AsymptSympt", "AsymptSympt2G",
                  "restenos", "stenose",
                  "CAD_history", "PAOD", "Peripheral.interv", 
                  "EP_composite", "Macrophages.bin", "SMC.bin",
                  "IPH.bin", 
                  "Calc.bin", "Collagen.bin", 
                  "Fat.bin_10", "Fat.bin_40", "OverallPlaquePhenotype")
# basetable_bin

basetable_con = basetable_vars[!basetable_vars %in% basetable_bin]
# basetable_con

3.2.3 Athero-Express Biobank Study: baseline characteristics

Showing the baseline table of the whole Athero-Express Biobank.

# Create baseline tables
# http://rstudio-pubs-static.s3.amazonaws.com/13321_da314633db924dc78986a850813a50d5.html
AEDB.CEA.tableOne = print(CreateTableOne(vars = basetable_vars, 
                                         # factorVars = basetable_bin,
                                         strata = "AsymptSympt2G",
                                         data = AEDB.CEA, includeNA = TRUE), 
                          nonnormal = c(), missing = TRUE,
                          quote = FALSE, noSpaces = FALSE, showAllLevels = TRUE, explain = TRUE, 
                          format = "pf", 
                          contDigits = 3)[,1:6]
                                      Stratified by AsymptSympt2G
                                       level                                                                     Asymptomatic      Symptomatic       p      test Missing
  n                                                                                                                  270              2151                              
  Hospital % (freq)                    St. Antonius, Nieuwegein                                                     43.3 (117)        38.6 ( 831)     0.154       0.0   
                                       UMC Utrecht                                                                  56.7 (153)        61.4 (1320)                       
  ORyear % (freq)                      No data available/missing                                                     0.0 (  0)         0.0 (   0)       NaN       0.0   
                                       2002                                                                          8.1 ( 22)         2.7 (  59)                       
                                       2003                                                                          8.9 ( 24)         6.2 ( 133)                       
                                       2004                                                                         12.2 ( 33)         7.3 ( 157)                       
                                       2005                                                                          8.5 ( 23)         7.5 ( 162)                       
                                       2006                                                                          6.7 ( 18)         7.7 ( 165)                       
                                       2007                                                                          6.7 ( 18)         6.2 ( 134)                       
                                       2008                                                                          5.2 ( 14)         5.8 ( 124)                       
                                       2009                                                                          7.0 ( 19)         7.5 ( 162)                       
                                       2010                                                                          5.9 ( 16)         6.6 ( 143)                       
                                       2011                                                                          4.8 ( 13)         7.0 ( 150)                       
                                       2012                                                                          5.2 ( 14)         7.5 ( 162)                       
                                       2013                                                                          3.7 ( 10)         6.5 ( 139)                       
                                       2014                                                                          7.4 ( 20)         6.6 ( 143)                       
                                       2015                                                                          1.9 (  5)         3.3 (  71)                       
                                       2016                                                                          0.7 (  2)         3.9 (  83)                       
                                       2017                                                                          4.1 ( 11)         2.5 (  54)                       
                                       2018                                                                          1.5 (  4)         2.9 (  62)                       
                                       2019                                                                          1.5 (  4)         2.2 (  48)                       
  Age (mean (SD))                                                                                                 66.481 (8.695)    69.434 (9.326)   <0.001       0.0   
  Gender % (freq)                      female                                                                       23.7 ( 64)        31.3 ( 674)     0.013       0.0   
                                       male                                                                         76.3 (206)        68.7 (1477)                       
  TC_finalCU (mean (SD))                                                                                         180.295 (46.689)  185.299 (57.212)   0.303      38.0   
  LDL_finalCU (mean (SD))                                                                                        103.501 (38.426)  108.963 (42.075)   0.155      45.6   
  HDL_finalCU (mean (SD))                                                                                         45.431 (14.817)   46.549 (17.235)   0.456      41.7   
  TG_finalCU (mean (SD))                                                                                         162.426 (90.058)  149.914 (91.364)   0.119      42.8   
  TC_final (mean (SD))                                                                                             4.670 (1.209)     4.799 (1.482)    0.303      38.0   
  LDL_final (mean (SD))                                                                                            2.681 (0.995)     2.822 (1.090)    0.155      45.6   
  HDL_final (mean (SD))                                                                                            1.177 (0.384)     1.206 (0.446)    0.456      41.7   
  TG_final (mean (SD))                                                                                             1.835 (1.018)     1.694 (1.032)    0.119      42.8   
  hsCRP_plasma (mean (SD))                                                                                         7.121 (24.154)   21.751 (247.521)  0.480      53.0   
  systolic (mean (SD))                                                                                           150.017 (23.846)  152.711 (25.312)   0.123      11.3   
  diastoli (mean (SD))                                                                                            79.618 (12.216)   81.525 (26.329)   0.275      11.3   
  GFR_MDRD (mean (SD))                                                                                            72.629 (21.381)   73.182 (21.128)   0.697       5.4   
  BMI (mean (SD))                                                                                                 26.712 (3.677)    26.460 (4.014)    0.336       5.9   
  KDOQI % (freq)                       No data available/missing                                                     0.0 (  0)         0.0 (   0)       NaN       5.5   
                                       Normal kidney function                                                       18.9 ( 51)        19.1 ( 411)                       
                                       CKD 2 (Mild)                                                                 47.0 (127)        51.4 (1105)                       
                                       CKD 3 (Moderate)                                                             25.6 ( 69)        22.5 ( 484)                       
                                       CKD 4 (Severe)                                                                0.0 (  0)         1.5 (  32)                       
                                       CKD 5 (Failure)                                                               0.7 (  2)         0.4 (   8)                       
                                       <NA>                                                                          7.8 ( 21)         5.2 ( 111)                       
  BMI_WHO % (freq)                     No data available/missing                                                     0.0 (  0)         0.0 (   0)       NaN       5.9   
                                       Underweight                                                                   0.7 (  2)         1.0 (  22)                       
                                       Normal                                                                       31.1 ( 84)        35.6 ( 766)                       
                                       Overweight                                                                   49.6 (134)        42.6 ( 917)                       
                                       Obese                                                                        14.4 ( 39)        14.6 ( 313)                       
                                       <NA>                                                                          4.1 ( 11)         6.2 ( 133)                       
  SmokerStatus % (freq)                Current smoker                                                               27.0 ( 73)        33.9 ( 730)     0.024       5.9   
                                       Ex-smoker                                                                    56.3 (152)        47.0 (1011)                       
                                       Never smoked                                                                 12.6 ( 34)        13.0 ( 279)                       
                                       <NA>                                                                          4.1 ( 11)         6.1 ( 131)                       
  AlcoholUse % (freq)                  No                                                                           34.8 ( 94)        34.4 ( 741)     0.631       4.0   
                                       Yes                                                                          62.2 (168)        61.4 (1320)                       
                                       <NA>                                                                          3.0 (  8)         4.2 (  90)                       
  DiabetesStatus % (freq)              Control (no Diabetes Dx/Med)                                                 76.3 (206)        75.0 (1614)     0.777       1.1   
                                       Diabetes                                                                     23.0 ( 62)        23.8 ( 512)                       
                                       <NA>                                                                          0.7 (  2)         1.2 (  25)                       
  Hypertension.selfreport % (freq)     No data available/missing                                                     0.0 (  0)         0.0 (   0)       NaN       3.2   
                                       no                                                                           23.7 ( 64)        24.4 ( 525)                       
                                       yes                                                                          74.1 (200)        72.2 (1554)                       
                                       <NA>                                                                          2.2 (  6)         3.3 (  72)                       
  Hypertension.selfreportdrug % (freq) No data available/missing                                                     0.0 (  0)         0.0 (   0)       NaN       4.4   
                                       no                                                                           30.0 ( 81)        29.9 ( 644)                       
                                       yes                                                                          65.2 (176)        65.7 (1413)                       
                                       <NA>                                                                          4.8 ( 13)         4.4 (  94)                       
  Hypertension.composite % (freq)      No data available/missing                                                     0.0 (  0)         0.0 (   0)       NaN       1.2   
                                       no                                                                           11.5 ( 31)        15.0 ( 322)                       
                                       yes                                                                          87.8 (237)        83.8 (1803)                       
                                       <NA>                                                                          0.7 (  2)         1.2 (  26)                       
  Hypertension.drugs % (freq)          No data available/missing                                                     0.0 (  0)         0.0 (   0)       NaN       1.4   
                                       no                                                                           17.8 ( 48)        24.0 ( 517)                       
                                       yes                                                                          81.1 (219)        74.6 (1604)                       
                                       <NA>                                                                          1.1 (  3)         1.4 (  30)                       
  Med.anticoagulants % (freq)          No data available/missing                                                     0.0 (  0)         0.0 (   0)       NaN       1.6   
                                       no                                                                           88.5 (239)        87.2 (1875)                       
                                       yes                                                                          10.4 ( 28)        11.2 ( 241)                       
                                       <NA>                                                                          1.1 (  3)         1.6 (  35)                       
  Med.all.antiplatelet % (freq)        No data available/missing                                                     0.0 (  0)         0.0 (   0)       NaN       1.5   
                                       no                                                                            7.4 ( 20)        12.8 ( 275)                       
                                       yes                                                                          91.1 (246)        85.7 (1844)                       
                                       <NA>                                                                          1.5 (  4)         1.5 (  32)                       
  Med.Statin.LLD % (freq)              No data available/missing                                                     0.0 (  0)         0.0 (   0)       NaN       1.4   
                                       no                                                                           16.7 ( 45)        20.7 ( 446)                       
                                       yes                                                                          82.2 (222)        77.8 (1674)                       
                                       <NA>                                                                          1.1 (  3)         1.4 (  31)                       
  Stroke_Dx % (freq)                   Missing                                                                       0.0 (  0)         0.0 (   0)       NaN       6.9   
                                       No stroke diagnosed                                                          80.7 (218)        70.3 (1513)                       
                                       Stroke diagnosed                                                             12.6 ( 34)        22.8 ( 490)                       
                                       <NA>                                                                          6.7 ( 18)         6.9 ( 148)                       
  sympt % (freq)                       missing                                                                       0.0 (  0)         0.0 (   0)       NaN       0.0   
                                       Asymptomatic                                                                100.0 (270)         0.0 (   0)                       
                                       TIA                                                                           0.0 (  0)        44.7 ( 961)                       
                                       minor stroke                                                                  0.0 (  0)        18.9 ( 407)                       
                                       Major stroke                                                                  0.0 (  0)        11.1 ( 238)                       
                                       Amaurosis fugax                                                               0.0 (  0)        17.6 ( 379)                       
                                       Four vessel disease                                                           0.0 (  0)         1.8 (  38)                       
                                       Vertebrobasilary TIA                                                          0.0 (  0)         0.2 (   5)                       
                                       Retinal infarction                                                            0.0 (  0)         1.6 (  34)                       
                                       Symptomatic, but aspecific symtoms                                            0.0 (  0)         2.5 (  53)                       
                                       Contralateral symptomatic occlusion                                           0.0 (  0)         0.5 (  11)                       
                                       retinal infarction                                                            0.0 (  0)         0.3 (   6)                       
                                       armclaudication due to occlusion subclavian artery, CEA needed for bypass     0.0 (  0)         0.0 (   1)                       
                                       retinal infarction + TIAs                                                     0.0 (  0)         0.0 (   0)                       
                                       Ocular ischemic syndrome                                                      0.0 (  0)         0.7 (  16)                       
                                       ischemisch glaucoom                                                           0.0 (  0)         0.0 (   0)                       
                                       subclavian steal syndrome                                                     0.0 (  0)         0.1 (   2)                       
                                       TGA                                                                           0.0 (  0)         0.0 (   0)                       
  Symptoms.5G % (freq)                 Asymptomatic                                                                100.0 (270)         0.0 (   0)    <0.001       0.0   
                                       Ocular                                                                        0.0 (  0)        18.4 ( 395)                       
                                       Other                                                                         0.0 (  0)         4.9 ( 105)                       
                                       Retinal infarction                                                            0.0 (  0)         1.9 (  40)                       
                                       Stroke                                                                        0.0 (  0)        30.0 ( 645)                       
                                       TIA                                                                           0.0 (  0)        44.9 ( 966)                       
  AsymptSympt % (freq)                 Asymptomatic                                                                100.0 (270)         0.0 (   0)    <0.001       0.0   
                                       Ocular and others                                                             0.0 (  0)        25.1 ( 540)                       
                                       Symptomatic                                                                   0.0 (  0)        74.9 (1611)                       
  AsymptSympt2G % (freq)               Asymptomatic                                                                100.0 (270)         0.0 (   0)    <0.001       0.0   
                                       Symptomatic                                                                   0.0 (  0)       100.0 (2151)                       
  restenos % (freq)                    missing                                                                       0.0 (  0)         0.0 (   0)       NaN       1.4   
                                       de novo                                                                      91.9 (248)        93.9 (2020)                       
                                       restenosis                                                                    6.3 ( 17)         4.7 ( 101)                       
                                       stenose bij angioseal na PTCA                                                 0.0 (  0)         0.0 (   0)                       
                                       <NA>                                                                          1.9 (  5)         1.4 (  30)                       
  stenose % (freq)                     missing                                                                       0.0 (  0)         0.0 (   0)       NaN       2.0   
                                       0-49%                                                                         0.0 (  0)         0.6 (  13)                       
                                       50-70%                                                                        2.6 (  7)         8.5 ( 182)                       
                                       70-90%                                                                       51.1 (138)        46.0 ( 989)                       
                                       90-99%                                                                       40.7 (110)        38.0 ( 817)                       
                                       100% (Occlusion)                                                              0.7 (  2)         1.3 (  29)                       
                                       NA                                                                            0.0 (  0)         0.0 (   1)                       
                                       50-99%                                                                        0.4 (  1)         0.7 (  14)                       
                                       70-99%                                                                        1.9 (  5)         2.9 (  63)                       
                                       99                                                                            0.0 (  0)         0.1 (   2)                       
                                       <NA>                                                                          2.6 (  7)         1.9 (  41)                       
  CAD_history % (freq)                 Missing                                                                       0.0 (  0)         0.0 (   0)       NaN       1.9   
                                       No history CAD                                                               57.8 (156)        68.0 (1462)                       
                                       History CAD                                                                  41.1 (111)        30.0 ( 645)                       
                                       <NA>                                                                          1.1 (  3)         2.0 (  44)                       
  PAOD % (freq)                        missing/no data                                                               0.0 (  0)         0.0 (   0)       NaN       2.0   
                                       no                                                                           71.9 (194)        78.2 (1682)                       
                                       yes                                                                          27.0 ( 73)        19.7 ( 424)                       
                                       <NA>                                                                          1.1 (  3)         2.1 (  45)                       
  Peripheral.interv % (freq)           no                                                                           66.3 (179)        78.5 (1689)    <0.001       2.9   
                                       yes                                                                          31.5 ( 85)        18.5 ( 397)                       
                                       <NA>                                                                          2.2 (  6)         3.0 (  65)                       
  EP_composite % (freq)                No data available.                                                            0.0 (  0)         0.0 (   0)       NaN       5.0   
                                       No composite endpoints                                                       66.3 (179)        71.1 (1530)                       
                                       Composite endpoints                                                          30.7 ( 83)        23.6 ( 507)                       
                                       <NA>                                                                          3.0 (  8)         5.3 ( 114)                       
  EP_composite_time (mean (SD))                                                                                    2.444 (1.032)     2.483 (1.119)    0.590       5.2   
  macmean0 (mean (SD))                                                                                             0.710 (0.999)     0.775 (1.207)    0.458      29.7   
  smcmean0 (mean (SD))                                                                                             2.395 (2.486)     1.928 (2.362)    0.009      29.9   
  Macrophages.bin % (freq)             no/minor                                                                     40.0 (108)        34.3 ( 738)     0.159      24.1   
                                       moderate/heavy                                                               38.9 (105)        41.2 ( 886)                       
                                       <NA>                                                                         21.1 ( 57)        24.5 ( 527)                       
  SMC.bin % (freq)                     no/minor                                                                     18.9 ( 51)        25.6 ( 551)     0.004      23.8   
                                       moderate/heavy                                                               60.7 (164)        50.1 (1078)                       
                                       <NA>                                                                         20.4 ( 55)        24.3 ( 522)                       
  neutrophils (mean (SD))                                                                                        120.955 (407.051) 151.585 (422.754)  0.655      87.4   
  Mast_cells_plaque (mean (SD))                                                                                  133.073 (145.270) 170.896 (166.898)  0.178      90.0   
  IPH.bin % (freq)                     no                                                                           36.7 ( 99)        30.0 ( 645)     0.065      23.5   
                                       yes                                                                          43.3 (117)        46.1 ( 991)                       
 [ reached getOption("max.print") -- omitted 23 rows ]

3.3 AESCRNA: baseline characteristics

metadata <- scRNAseqData@meta.data %>% as_tibble()
scRNAseqDataMeta <- metadata %>% distinct(Patient, .keep_all = TRUE)
distinct: removed 6,154 rows (99%), 37 rows remaining
scRNAseqDataMetaAE <- merge(scRNAseqDataMeta, AEDB, by.x = "Patient", by.y = "STUDY_NUMBER", sort = FALSE, all.x = TRUE)
dim(scRNAseqDataMetaAE)
[1]   37 1123
# Replace missing data 
# Ref: https://cran.r-project.org/web/packages/naniar/vignettes/replace-with-na.html
require(naniar)

na_strings <- c("NA", "N A", "N / A", "N/A", "N/ A", 
                "Not Available", "Not available", 
                "missing", 
                "-999", "-99", 
                "No data available/missing", "No data available/Missing")
# Then you write ~.x %in% na_strings - which reads as “does this value occur in the list of NA strings”.

scRNAseqDataMetaAE %>%
  replace_with_na_all(condition = ~.x %in% na_strings)

cat("====================================================================================================")
====================================================================================================
cat("SELECTION THE SHIZZLE")
SELECTION THE SHIZZLE
cat("- sanity checking PRIOR to selection")
- sanity checking PRIOR to selection
library(data.table)
require(labelled)
ae.gender <- to_factor(scRNAseqDataMetaAE$Gender)
ae.hospital <- to_factor(scRNAseqDataMetaAE$Hospital)
table(ae.gender, ae.hospital, dnn = c("Sex", "Hospital"), useNA = "ifany")
        Hospital
Sex      St. Antonius, Nieuwegein UMC Utrecht <NA>
  female                        0          10    0
  male                          0          26    0
  <NA>                          0           0    1
ae.artery <- to_factor(scRNAseqDataMetaAE$Artery_summary)
table(ae.artery, ae.gender, dnn = c("Sex", "Artery"), useNA = "ifany")
                                                                                         Artery
Sex                                                                                       female male <NA>
  No artery known (yet), no surgery (patient ill, died, exited study), re-numbered to AAA      0    0    0
  carotid (left & right)                                                                      10   25    0
  femoral/iliac (left, right or both sides)                                                    0    0    0
  other carotid arteries (common, external)                                                    0    1    0
  carotid bypass and injury (left, right or both sides)                                        0    0    0
  aneurysmata (carotid & femoral)                                                              0    0    0
  aorta                                                                                        0    0    0
  other arteries (renal, popliteal, vertebral)                                                 0    0    0
  femoral bypass, angioseal and injury (left, right or both sides)                             0    0    0
  <NA>                                                                                         0    0    1
ae.ic <- to_factor(scRNAseqDataMetaAE$informedconsent)
table(ae.ic, ae.gender, useNA = "ifany")
                                                                                                 ae.gender
ae.ic                                                                                             female male <NA>
  missing                                                                                              0    0    0
  no, died                                                                                             0    0    0
  yes                                                                                                  5   14    0
  yes, health treatment when possible                                                                  2    7    0
  yes, no health treatment                                                                             1    2    0
  yes, no health treatment, no commercial business                                                     1    2    0
  yes, no tissue, no commerical business                                                               0    0    0
  yes, no tissue, no questionnaires, no medical info, no commercial business                           0    0    0
  yes, no questionnaires, no health treatment, no commercial business                                  0    0    0
  yes, no questionnaires, health treatment when possible                                               0    0    0
  yes, no tissue, no questionnaires, no health treatment, no commerical business                       0    0    0
  yes, no health treatment, no medical info, no commercial business                                    0    0    0
  yes, no tissue, no questionnaires, no health treatment, no medical info, no commercial business      0    0    0
  yes, no questionnaires, no health treatment                                                          0    0    0
  yes, no tissue, no health treatment                                                                  0    0    0
  yes, no tissue, no questionnaires                                                                    0    0    0
  yes, no tissue, health treatment when possible                                                       0    0    0
  yes, no tissue                                                                                       0    0    0
  yes, no commerical business                                                                          0    1    0
  yes, health treatment when possible, no commercial business                                          0    0    0
  yes, no medical info, no commercial business                                                         0    0    0
  yes, no questionnaires                                                                               0    0    0
  yes, no tissue, no questionnaires, no health treatment, no medical info                              0    0    0
  yes, no tissue, no questionnaires, no health treatment, no commercial business                       0    0    0
  yes, no medical info                                                                                 0    0    0
  yes, no questionnaires, no commercial business                                                       0    0    0
  yes, no questionnaires, no health treatment, no medical info                                         0    0    0
  yes, no questionnaires, health treatment when possible, no commercial business                       0    0    0
  yes,  no health treatment, no medical info                                                           0    0    0
  no, doesn't want to                                                                                  0    0    0
  no, unable to sign                                                                                   0    0    0
  no, no reaction                                                                                      0    0    0
  no, lost                                                                                             0    0    0
  no, too old                                                                                          0    0    0
  yes, no medical info, health treatment when possible                                                 1    0    0
  no (never asked for IC because there was no tissue)                                                  0    0    0
  yes, no medical info, no commercial business, health treatment when possible                         0    0    0
  no, endpoint                                                                                         0    0    0
  wil niets invullen, wel alles gebruiken                                                              0    0    0
  second informed concents: yes, no commercial business                                                0    0    0
  nooit geincludeerd                                                                                   0    0    0
  <NA>                                                                                                 0    0    1
rm(ae.gender, ae.hospital, ae.artery, ae.ic)


scRNAseqDataMetaAE.all <- subset(scRNAseqDataMetaAE,
                            (Artery_summary == "carotid (left & right)" | Artery_summary == "other carotid arteries (common, external)" ) & # we only want carotids
                              informedconsent != "missing" & # we are really strict in selecting based on 'informed consent'!
                              informedconsent != "no, died" &
                              informedconsent != "yes, no tissue, no commerical business" &
                              informedconsent != "yes, no tissue, no questionnaires, no medical info, no commercial business" &
                              informedconsent != "yes, no tissue, no questionnaires, no health treatment, no commerical business" &
                              informedconsent != "yes, no tissue, no questionnaires, no health treatment, no medical info, no commercial business" &
                              informedconsent != "yes, no tissue, no health treatment" &
                              informedconsent != "yes, no tissue, no questionnaires" &
                              informedconsent != "yes, no tissue, health treatment when possible" &
                              informedconsent != "yes, no tissue" &
                              informedconsent != "yes, no tissue, no questionnaires, no health treatment, no medical info" &
                              informedconsent != "yes, no tissue, no questionnaires, no health treatment, no commercial business" &
                              informedconsent != "no, doesn't want to" &
                              informedconsent != "no, unable to sign" &
                              informedconsent != "no, no reaction" &
                              informedconsent != "no, lost" &
                              informedconsent != "no, too old" &
                              informedconsent != "yes, no medical info, health treatment when possible" & 
                              informedconsent != "no (never asked for IC because there was no tissue)" &
                              informedconsent != "no, endpoint" &
                              informedconsent != "nooit geincludeerd")
# scRNAseqDataMetaAE.all[1:10, 1:10]
dim(scRNAseqDataMetaAE.all)
[1]   35 1123
# DT::datatable(scRNAseqDataMetaAE.all)

Showing the baseline table.

cat("===========================================================================================")
===========================================================================================
cat("CREATE BASELINE TABLE")
CREATE BASELINE TABLE
# Create baseline tables
# http://rstudio-pubs-static.s3.amazonaws.com/13321_da314633db924dc78986a850813a50d5.html
scRNAseqDataMetaAE.all.tableOne = print(CreateTableOne(vars = basetable_vars, 
                                                  # factorVars = basetable_bin,
                                                  strata = "AsymptSympt2G", 
                                                  data = scRNAseqDataMetaAE.all, includeNA = TRUE), 
                                   nonnormal = c(), 
                                   quote = FALSE, showAllLevels = TRUE,
                                   format = "p", 
                                   contDigits = 3)[,1:2]
These variables only have NA/NaN: hsCRP_plasma macmean0 smcmean0 Macrophages.bin SMC.bin neutrophils Mast_cells_plaque IPH.bin vessel_density_averaged Calc.bin Collagen.bin Fat.bin_10 Fat.bin_40 OverallPlaquePhenotype IL6 IL6_pg_ug_2015 IL6R_pg_ug_2015 MCP1 MCP1_pg_ug_2015  Dropped
                                 Stratified by AsymptSympt2G
                                  level                                                                     Asymptomatic      Symptomatic      p      test
  n                                                                                                               6                29                     
  Hospital (%)                    St. Antonius, Nieuwegein                                                      0.0               0.0             NaN     
                                  UMC Utrecht                                                                 100.0             100.0                     
  ORyear (%)                      No data available/missing                                                     0.0               0.0             NaN     
                                  2002                                                                          0.0               0.0                     
                                  2003                                                                          0.0               0.0                     
                                  2004                                                                          0.0               0.0                     
                                  2005                                                                          0.0               0.0                     
                                  2006                                                                          0.0               0.0                     
                                  2007                                                                          0.0               0.0                     
                                  2008                                                                          0.0               0.0                     
                                  2009                                                                          0.0               0.0                     
                                  2010                                                                          0.0               0.0                     
                                  2011                                                                          0.0               0.0                     
                                  2012                                                                          0.0               0.0                     
                                  2013                                                                          0.0               0.0                     
                                  2014                                                                          0.0               0.0                     
                                  2015                                                                          0.0               0.0                     
                                  2016                                                                          0.0               0.0                     
                                  2017                                                                          0.0               0.0                     
                                  2018                                                                         66.7              65.5                     
                                  2019                                                                         33.3              34.5                     
  Age (mean (SD))                                                                                            68.333 (5.354)    73.655 (8.427)   0.149     
  Gender (%)                      female                                                                        0.0              31.0           0.285     
                                  male                                                                        100.0              69.0                     
  TC_finalCU (mean (SD))                                                                                    148.005 (52.704)  170.849 (47.727)  0.446     
  LDL_finalCU (mean (SD))                                                                                    59.202 (5.898)   101.897 (37.879)  0.067     
  HDL_finalCU (mean (SD))                                                                                    37.066 (7.837)    44.788 (10.236)  0.223     
  TG_finalCU (mean (SD))                                                                                    260.767 (195.784) 159.292 (94.171)  0.137     
  TC_final (mean (SD))                                                                                        3.833 (1.365)     4.425 (1.236)   0.446     
  LDL_final (mean (SD))                                                                                       1.533 (0.153)     2.639 (0.981)   0.067     
  HDL_final (mean (SD))                                                                                       0.960 (0.203)     1.160 (0.265)   0.223     
  TG_final (mean (SD))                                                                                        2.947 (2.212)     1.800 (1.064)   0.137     
  systolic (mean (SD))                                                                                      150.800 (25.821)  153.690 (26.233)  0.821     
  diastoli (mean (SD))                                                                                       83.200 (5.762)    80.414 (17.340)  0.727     
  GFR_MDRD (mean (SD))                                                                                       66.437 (28.718)   85.083 (31.324)  0.333     
  BMI (mean (SD))                                                                                            26.412 (2.710)    26.492 (3.620)   0.960     
  KDOQI (%)                       No data available/missing                                                     0.0               0.0             NaN     
                                  Normal kidney function                                                       16.7              37.9                     
                                  CKD 2 (Mild)                                                                  0.0              37.9                     
                                  CKD 3 (Moderate)                                                             33.3              20.7                     
                                  CKD 4 (Severe)                                                                0.0               0.0                     
                                  CKD 5 (Failure)                                                               0.0               0.0                     
                                  <NA>                                                                         50.0               3.4                     
  BMI_WHO (%)                     No data available/missing                                                     0.0               0.0             NaN     
                                  Underweight                                                                   0.0               3.4                     
                                  Normal                                                                       33.3              31.0                     
                                  Overweight                                                                   50.0              41.4                     
                                  Obese                                                                        16.7              13.8                     
                                  <NA>                                                                          0.0              10.3                     
  SmokerStatus (%)                Current smoker                                                               16.7              37.9           0.296     
                                  Ex-smoker                                                                    83.3              41.4                     
                                  Never smoked                                                                  0.0              17.2                     
                                  <NA>                                                                          0.0               3.4                     
  AlcoholUse (%)                  No                                                                            0.0              44.8           0.066     
                                  Yes                                                                         100.0              48.3                     
                                  <NA>                                                                          0.0               6.9                     
  DiabetesStatus (%)              Control (no Diabetes Dx/Med)                                                 83.3              58.6           0.499     
                                  Diabetes                                                                     16.7              41.4                     
  Hypertension.selfreport (%)     No data available/missing                                                     0.0               0.0             NaN     
                                  no                                                                            0.0              13.8                     
                                  yes                                                                         100.0              82.8                     
                                  <NA>                                                                          0.0               3.4                     
  Hypertension.selfreportdrug (%) No data available/missing                                                     0.0               0.0             NaN     
                                  no                                                                           16.7              10.3                     
                                  yes                                                                          83.3              86.2                     
                                  <NA>                                                                          0.0               3.4                     
  Hypertension.composite (%)      No data available/missing                                                     0.0               0.0             NaN     
                                  no                                                                            0.0               6.9                     
                                  yes                                                                         100.0              93.1                     
  Hypertension.drugs (%)          No data available/missing                                                     0.0               0.0             NaN     
                                  no                                                                           16.7               3.4                     
                                  yes                                                                          83.3              93.1                     
                                  <NA>                                                                          0.0               3.4                     
  Med.anticoagulants (%)          No data available/missing                                                     0.0               0.0             NaN     
                                  no                                                                          100.0              86.2                     
                                  yes                                                                           0.0               6.9                     
                                  <NA>                                                                          0.0               6.9                     
  Med.all.antiplatelet (%)        No data available/missing                                                     0.0               0.0             NaN     
                                  no                                                                            0.0              31.0                     
                                  yes                                                                         100.0              65.5                     
                                  <NA>                                                                          0.0               3.4                     
  Med.Statin.LLD (%)              No data available/missing                                                     0.0               0.0             NaN     
                                  no                                                                            0.0              24.1                     
                                  yes                                                                         100.0              72.4                     
                                  <NA>                                                                          0.0               3.4                     
  Stroke_Dx (%)                   Missing                                                                       0.0               0.0             NaN     
                                  No stroke diagnosed                                                          66.7              48.3                     
                                  Stroke diagnosed                                                             33.3              51.7                     
  sympt (%)                       missing                                                                       0.0               0.0             NaN     
                                  Asymptomatic                                                                100.0               0.0                     
                                  TIA                                                                           0.0              17.2                     
                                  minor stroke                                                                  0.0              41.4                     
                                  Major stroke                                                                  0.0              10.3                     
                                  Amaurosis fugax                                                               0.0              17.2                     
                                  Four vessel disease                                                           0.0               0.0                     
                                  Vertebrobasilary TIA                                                          0.0               0.0                     
                                  Retinal infarction                                                            0.0               3.4                     
                                  Symptomatic, but aspecific symtoms                                            0.0               0.0                     
                                  Contralateral symptomatic occlusion                                           0.0               0.0                     
                                  retinal infarction                                                            0.0               3.4                     
                                  armclaudication due to occlusion subclavian artery, CEA needed for bypass     0.0               0.0                     
                                  retinal infarction + TIAs                                                     0.0               0.0                     
                                  Ocular ischemic syndrome                                                      0.0               6.9                     
                                  ischemisch glaucoom                                                           0.0               0.0                     
                                  subclavian steal syndrome                                                     0.0               0.0                     
                                  TGA                                                                           0.0               0.0                     
  Symptoms.5G (%)                 Asymptomatic                                                                100.0               0.0             NaN     
                                  Ocular                                                                        0.0              24.1                     
                                  Other                                                                         0.0               0.0                     
                                  Retinal infarction                                                            0.0               6.9                     
                                  Stroke                                                                        0.0              51.7                     
                                  TIA                                                                           0.0              17.2                     
  AsymptSympt (%)                 Asymptomatic                                                                100.0               0.0          <0.001     
                                  Ocular and others                                                             0.0              31.0                     
                                  Symptomatic                                                                   0.0              69.0                     
  AsymptSympt2G (%)               Asymptomatic                                                                100.0               0.0          <0.001     
                                  Symptomatic                                                                   0.0             100.0                     
  restenos (%)                    missing                                                                       0.0               0.0             NaN     
                                  de novo                                                                     100.0             100.0                     
                                  restenosis                                                                    0.0               0.0                     
                                  stenose bij angioseal na PTCA                                                 0.0               0.0                     
  stenose (%)                     missing                                                                       0.0               0.0             NaN     
                                  0-49%                                                                         0.0               3.4                     
                                  50-70%                                                                       16.7              17.2                     
                                  70-90%                                                                       16.7              48.3                     
                                  90-99%                                                                       33.3              13.8                     
                                  100% (Occlusion)                                                              0.0               0.0                     
                                  NA                                                                            0.0               0.0                     
                                  50-99%                                                                        0.0               0.0                     
                                  70-99%                                                                       33.3              17.2                     
                                  99                                                                            0.0               0.0                     
  CAD_history (%)                 Missing                                                                       0.0               0.0             NaN     
                                  No history CAD                                                              100.0              69.0                     
                                  History CAD                                                                   0.0              31.0                     
  PAOD (%)                        missing/no data                                                               0.0               0.0             NaN     
                                  no                                                                           50.0              93.1                     
                                  yes                                                                          50.0               6.9                     
  Peripheral.interv (%)           no                                                                           50.0              82.8           0.125     
                                  yes                                                                          50.0              13.8                     
                                  <NA>                                                                          0.0               3.4                     
  EP_composite (%)                No data available.                                                            0.0               0.0             NaN     
                                  No composite endpoints                                                       66.7              37.9                     
                                  Composite endpoints                                                           0.0              13.8                     
                                  <NA>                                                                         33.3              48.3                     
  EP_composite_time (mean (SD))                                                                               1.326 (0.557)     0.826 (0.421)   0.064     

Writing the baseline table to Excel format.

# Write basetable
require(openxlsx)
write.xlsx(file = paste0(OUT_loc, "/",Today,".",PROJECTNAME,".AE.BaselineTable.scRNAseq.xlsx"), 
           format(scRNAseqDataMetaAE.all.tableOne, digits = 5, scientific = FALSE) , row.names = TRUE, col.names = TRUE)

4 AESCRNA

4.1 Quality control

Here review the number of cells per sample, plate, and patients. And plot the ratio’s per sample and study number.

## check stuff
cat("\nHow many cells per type ...?")

How many cells per type ...?
sort(table(scRNAseqData@meta.data$SCT_snn_res.0.8))

  17   16   15   14   13   12   11   10    9    8    7    6    5    4    3    2    1    0 
  31   34   84  110  151  172  190  203  211  225  290  345  437  534  577  626  861 1110 
cat("\n\nHow many cells per plate ...?")


How many cells per plate ...?
sort(table(scRNAseqData@meta.data$ID))

4530.P1 4440.P1 4472.P1 4478.P1 4477.P1 4500.P1 4458.P1 4459.P1 4447.P2 4447.P3 4487.P2 4502.P1 4455.P1 4496.P1 4501.P1 4447.P1 4489.P1 4476.P1 4448.P1 4487.P1 4571.P1 4495.P1 
      4      11      32      40      41      45      47      48      51      66      75      80      82      88      93      96     102     104     105     112     112     115 
4432.P1 4520.P1 4450.P2 4545.P1 4513.P1 4452.P3 4453.P3 4452.P2 4450.P1 4558.P1 4535.P1 4488.P1 4480.P1 4470.P1 4450.P3 4453.P1 4486.P1 4452.P1 4546.P1 4443.P2 4491.P1 4453.P2 
    129     130     134     135     139     141     143     155     157     157     158     159     161     165     166     177     179     183     188     189     193     197 
4530.P2 4443.P1 4521.P2 4443.P3 4542.P1 
    207     209     212     239     240 
cat("\n\nHow many cells per type per plate ...?")


How many cells per type per plate ...?
table(scRNAseqData@meta.data$SCT_snn_res.0.8, scRNAseqData@meta.data$ID)
    
     4432.P1 4440.P1 4443.P1 4443.P2 4443.P3 4447.P1 4447.P2 4447.P3 4448.P1 4450.P1 4450.P2 4450.P3 4452.P1 4452.P2 4452.P3 4453.P1 4453.P2 4453.P3 4455.P1 4458.P1 4459.P1
  0       28       2      31      56      53       8       7      12      22      15      22      22       5      15      21      19      20      23      10       5       2
  1       23       0       0       1       0       0       0       0      27      13      10      11       7      17      14      18      27      17       9       2       9
  2       18       1      15      11       8       9       5       4      11      20      23      25       4       8       8       7       4       6      19       8       9
  3        2       0       3       4       4       4       3       8       0      27      20      50     134      48      32      42      78      12       2       2       0
  4       14       2       5       7       5       2       3       2       6      12       5       5       7      10       6      33      15      19      10       1       2
  5       11       3      21      22      28       5       6       5       7       9       4       5       5       6       4      17      14      14       5       6       6
  6        1       1      66      46      73      15       1       4       3       4       7       6       4      10      13      18      10      26       3       1       3
  7        4       0      14       6       4       8       4       6       3      31      24      10       5       9      14       4       2       5       0       2       1
  8        9       0      10       2       5       4       3       7       4       2       5       4       0       2       2       1       3       1       4       2       0
  9        3       1       3       2       2       8       5       8       8      14       4       8       2       3       2       0       0       2       1       8       0
  10       7       0       5       6       1       2       2       0       2       2       3       6       3       4       2       1       4       2       7       2       2
  11       0       1       7       5       2      23      11       8       0       0       5       3       0       8       7       1       2       5       3       2       6
  12       4       0       8       7      11       0       0       0       9       0       0       5       3       6       6       2      11       3       3       0       4
  13       1       0      20       7      40       6       0       0       1       0       1       1       0       0       1       0       1       0       2       1       1
  14       1       0       0       1       1       1       0       1       1       5       1       3       1       2       6       4       3       1       2       3       0
  15       0       0       1       5       1       1       1       1       0       1       0       1       3       6       2       4       3       3       1       2       1
  16       1       0       0       0       1       0       0       0       0       0       0       0       0       0       1       2       0       3       0       0       2
  17       2       0       0       1       0       0       0       0       1       2       0       1       0       1       0       4       0       1       1       0       0
    
     4470.P1 4472.P1 4476.P1 4477.P1 4478.P1 4480.P1 4486.P1 4487.P1 4487.P2 4488.P1 4489.P1 4491.P1 4495.P1 4496.P1 4500.P1 4501.P1 4502.P1 4513.P1 4520.P1 4521.P2 4530.P1
  0       44       1      10      11       7      15      38      15       5      28      30      38      12       7       9      28      25      29      23      43       0
  1       25       5      21      12       7      51      21      27      21      44      20      51      29       9       4      28      17      24      20      48       1
  2       16      10       8       2       6      16      31      18      11      10       4      11      14      49       5      10       1       9      13      13       1
  3        2       0       7       0       1       3       7       0      14       1       0       1       2       3       6       3       2       2       7      14       1
  4       23       0       7       2       1       1       7       1       2       4      11      16      15       3       3       2      17      29       8      32       0
  5        5       3      13       1       5      17       5      11       4       8       4      22      14       4       8       3       5      17       7      14       1
  6       20       0       0       0       0       0       1       9       0       0       0       0       0       0       0       0       0       0       0       0       0
  7        7       3       3       0       1      12      19       5       0      18       4       2       3       1       1       3       0       1       1       0       0
  8        4       3       3       1       4       2      12       7       2       6       9       5       6       1       0       1       2      14      19       1       0
  9        0       0       1       0       1       3      14       6       1      17       4       9       1       2       0       1       1       4       7       2       0
  10       1       4       4       1       0      12       3       2       1       2       4      18       7       3       0       7       1       4       3       4       0
  11       7       1      10       5       1       9       8       0      11       2       3       2       5       0       2       5       2       1       7       5       0
  12       7       1       2       5       4       8       4       4       1      16       2       9       1       0       0       0       7       4       0       6       0
  13       2       0       1       0       0       0       1       3       0       1       0       3       0       1       0       0       0       0       1      20       0
  14       0       0       9       0       1       2       0       1       1       1       3       1       4       1       5       0       0       1      11       2       0
  15       2       1       2       0       1       8       6       2       1       0       1       4       1       2       0       1       0       0       1       6       0
  16       0       0       2       0       0       2       2       0       0       1       0       0       1       1       2       1       0       0       0       1       0
  17       0       0       1       1       0       0       0       1       0       0       3       1       0       1       0       0       0       0       2       1       0
    
     4530.P2 4535.P1 4542.P1 4545.P1 4546.P1 4558.P1 4571.P1
  0       29      32      78      57      44      44      10
  1       12      24      41      17      38      28      11
  2       72      12      11       1       8      11      30
  3        9       1       2       6       3       3       2
  4       17      45      13      14      64      22       4
  5       18      12      12       3       4       3      11
  6        0       0       0       0       0       0       0
  7        3       1      23       4       4       7       8
  8       25       2       5       8       1       3       9
  9        2       0      37       5       2       5       2
  10       4      12       5       6      12      19       1
  11       0       2       1       1       1       0       0
  12       0       4       1       1       2       1       0
  13       5       0       3       5       1       6      15
  14       6       7       2       6       3       2       4
  15       0       2       2       0       1       2       1
  16       1       2       3       1       0       0       4
  17       4       0       1       0       0       1       0
cat("\n\nHow many cells per patient ...?")


How many cells per patient ...?
sort(table(scRNAseqData@meta.data$Patient))

4440 4472 4478 4477 4500 4458 4459 4502 4455 4496 4501 4489 4476 4448 4571 4495 4432 4520 4545 4513 4558 4535 4488 4480 4470 4486 4487 4546 4491 4530 4521 4447 4542 4450 4452 
  11   32   40   41   45   47   48   80   82   88   93  102  104  105  112  115  129  130  135  139  157  158  159  161  165  179  187  188  193  211  212  213  240  457  479 
4453 4443 
 517  637 
cat("\n\nVisualizing these ratio's per study number and sample ...?")


Visualizing these ratio's per study number and sample ...?
UMAPPlot(scRNAseqData, label = TRUE, pt.size = 1.25, label.size = 4, group.by = "ident",
         repel = TRUE)
ggsave(paste0(PLOT_loc, "/", Today, ".UMAP.png"), plot = last_plot())
Saving 7.29 x 4.51 in image
ggsave(paste0(PLOT_loc, "/", Today, ".UMAP.ps"), plot = last_plot())
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barplot(prop.table(x = table(scRNAseqData@active.ident, scRNAseqData@meta.data$Patient)), 
        cex.axis = 1.0, cex.names = 0.5, las = 1,
        col = uithof_color, xlab = "study number", legend.text = FALSE, args.legend = list(x = "bottom"))
dev.copy(pdf, paste0(QC_loc, "/", Today, ".cell_ratios_per_sample.pdf"))
pdf 
  3 
dev.off()
quartz_off_screen 
                2 

barplot(prop.table(x = table(scRNAseqData@active.ident, scRNAseqData@meta.data$ID)), 
        cex.axis = 1.0, cex.names = 0.5, las = 2,
        col = uithof_color, xlab = "sample ID", legend.text = FALSE, args.legend = list(x = "bottom"))
dev.copy(pdf, paste0(QC_loc, "/", Today, ".cell_ratios_per_sample_per_plate.pdf"))
pdf 
  3 
dev.off()
quartz_off_screen 
                2 

4.2 Visualisations

Let’s project known cellular markers.


UMAPPlot(scRNAseqData, label = FALSE, pt.size = 1.25, label.size = 4, group.by = "ident",
         repel = TRUE)


# endothelial cells
FeaturePlot(scRNAseqData, features = c("CD34"), cols =  c("#ECECEC", "#DB003F"))

FeaturePlot(scRNAseqData, features = c("EDN1"), cols =  c("#ECECEC", "#DB003F"))

FeaturePlot(scRNAseqData, features = c("EDNRA", "EDNRB"), cols =  c("#ECECEC", "#DB003F"))

FeaturePlot(scRNAseqData, features = c("CDH5", "PECAM1"), cols =  c("#ECECEC", "#DB003F"))

FeaturePlot(scRNAseqData, features = c("ACKR1"), cols =  c("#ECECEC", "#DB003F"))


# SMC
FeaturePlot(scRNAseqData, features = c("MYH11"), cols =  c("#ECECEC", "#DB003F"))

FeaturePlot(scRNAseqData, features = c("LGALS3", "ACTA2"), cols =  c("#ECECEC", "#DB003F"))


# macrophages
FeaturePlot(scRNAseqData, features = c("CD14", "CD68"), cols =  c("#ECECEC", "#DB003F"))

FeaturePlot(scRNAseqData, features = c("CD36"), cols =  c("#ECECEC", "#DB003F"))


# t-cells
FeaturePlot(scRNAseqData, features = c("CD3E"), cols =  c("#ECECEC", "#DB003F"))

FeaturePlot(scRNAseqData, features = c("CD4"), cols =  c("#ECECEC", "#DB003F"))

# FeaturePlot(scRNAseqData, features = c("CD8"), cols =  c("#ECECEC", "#DB003F"))

# b-cells
FeaturePlot(scRNAseqData, features = c("CD79A"), cols =  c("#ECECEC", "#DB003F"))


# mast cells
FeaturePlot(scRNAseqData, features = c("KIT"), cols =  c("#ECECEC", "#DB003F"))


# NK cells
FeaturePlot(scRNAseqData, features = c("NCAM1"), cols =  c("#ECECEC", "#DB003F"))

4.3 Targets of interest: MCP1, CCR2, IL6, IL6R

We check whether the targets genes, MCP1, CCR2, IL6, IL6R, were sequenced using our method (STARseq). Given that COL4A1/2 are downstream targets of MMP2 we also checked the cell type specific expression of these genes.

4.3.1 Expression in cell communities

UMAPPlot(scRNAseqData, label = FALSE, pt.size = 1.25, label.size = 4, group.by = "ident",
         repel = TRUE)


VlnPlot(scRNAseqData, features = TARGET_A_alt) + 
  xlab("cell communities") + 
  ylab(bquote("normalized "~italic(CCL2)~" expression")) +
  theme(
    axis.title.x = element_text(color = "#000000", size = 14, face = "bold"),
    axis.title.y = element_text(color = "#000000", size = 14, face = "bold"), 
    legend.position = "none")
ggsave(paste0(PLOT_loc, "/", Today, ".VlnPlot.",TARGET_A,".png"), plot = last_plot())
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ggsave(paste0(PLOT_loc, "/", Today, ".VlnPlot.",TARGET_A,".ps"), plot = last_plot())
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VlnPlot(scRNAseqData, features = TARGET_B) + 
  xlab("cell communities") + 
  ylab(bquote("normalized "~italic(CCR2)~" expression")) +
  theme(
    axis.title.x = element_text(color = "#000000", size = 14, face = "bold"),
    axis.title.y = element_text(color = "#000000", size = 14, face = "bold"), 
    legend.position = "none")
ggsave(paste0(PLOT_loc, "/", Today, ".VlnPlot.",TARGET_B,".png"), plot = last_plot())
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ggsave(paste0(PLOT_loc, "/", Today, ".VlnPlot.",TARGET_B,".ps"), plot = last_plot())
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# VlnPlot(scRNAseqData, features = TARGET_C) + 
#   xlab("cell communities") + 
#   ylab(bquote("normalized "~italic(IL6)~" expression")) +
#   theme(
#     axis.title.x = element_text(color = "#000000", size = 14, face = "bold"),
#     axis.title.y = element_text(color = "#000000", size = 14, face = "bold"), 
#     legend.position = "none")
# ggsave(paste0(PLOT_loc, "/", Today, ".VlnPlot.",TARGET_C,".png"), plot = last_plot())
# ggsave(paste0(PLOT_loc, "/", Today, ".VlnPlot.",TARGET_C,".ps"), plot = last_plot())
# 
# VlnPlot(scRNAseqData, features = TARGET_D) + 
#   xlab("cell communities") + 
#   ylab(bquote("normalized "~italic(IL6R)~" expression")) +
#   theme(
#     axis.title.x = element_text(color = "#000000", size = 14, face = "bold"),
#     axis.title.y = element_text(color = "#000000", size = 14, face = "bold"), 
#     legend.position = "none")
# ggsave(paste0(PLOT_loc, "/", Today, ".VlnPlot.",TARGET_D,".png"), plot = last_plot())
# ggsave(paste0(PLOT_loc, "/", Today, ".VlnPlot.",TARGET_D,".ps"), plot = last_plot())

# GenesOfInterest_TargetsA <- c("CCL2", "CCR2", "IL6", "IL6R", "ACTA2", "MYH11", "CD34", "CD14", "CD68", "CD3E", "CD4")
# GenesOfInterest_TargetsA <- c("CCL2", "CCR2", "IL6", "IL6R")
GenesOfInterest_TargetsA <- c("CCL2", "CCR2")

library(RColorBrewer)

p1 <- DotPlot(scRNAseqData, features = GenesOfInterest_TargetsA, 
        cols = "RdBu")

p1 + theme(axis.text.x = element_text(angle = 45, hjust=1))

ggsave(paste0(PLOT_loc, "/", Today, ".DotPlot.Targets.png"), plot = last_plot())
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ggsave(paste0(PLOT_loc, "/", Today, ".DotPlot.Targets.ps"), plot = last_plot())
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rm(p1)

# Visualize co-expression of two features simultaneously

FeaturePlot(scRNAseqData, features = c(TARGET_A_alt, "CD14"), 
            # cols = c("#ECECEC", "#DB003F", "#9A3480","#1290D9"),
            # combine = TRUE, 
            blend = TRUE)

FeaturePlot(scRNAseqData, features = c(TARGET_A_alt, "CD68"), 
            # cols = c("#ECECEC", "#DB003F", "#9A3480","#1290D9"),
            # combine = TRUE, 
            blend = TRUE)


FeaturePlot(scRNAseqData, features = c(TARGET_B, "CD14"), 
            # cols = c("#ECECEC", "#DB003F", "#9A3480","#1290D9"),
            # combine = TRUE, 
            blend = TRUE)

FeaturePlot(scRNAseqData, features = c(TARGET_B, "CD68"), 
            # cols = c("#ECECEC", "#DB003F", "#9A3480","#1290D9"),
            # combine = TRUE, 
            blend = TRUE)


FeaturePlot(scRNAseqData, features = c(TARGET_B, "CD79A"), 
            # cols = c("#ECECEC", "#DB003F", "#9A3480","#1290D9"),
            # combine = TRUE, 
            blend = TRUE)

FeaturePlot(scRNAseqData, features = c(TARGET_B, "CD79A"), 
            # cols = c("#ECECEC", "#DB003F", "#9A3480","#1290D9"),
            # combine = TRUE, 
            blend = TRUE)


# FeaturePlot(scRNAseqData, features = c(TARGET_D, "CD79A"), 
#             # cols = c("#ECECEC", "#DB003F", "#9A3480","#1290D9"),
#             # combine = TRUE, 
#             blend = TRUE)
# FeaturePlot(scRNAseqData, features = c(TARGET_D, "CD79A"), 
#             # cols = c("#ECECEC", "#DB003F", "#9A3480","#1290D9"),
#             # combine = TRUE, 
#             blend = TRUE)

FeaturePlot(scRNAseqData, features = c(GenesOfInterest_TargetsA), 
            cols =  c("#ECECEC", "#DB003F", "#9A3480","#1290D9"), 
            combine = TRUE)

ggsave(paste0(PLOT_loc, "/", Today, ".FeaturePlot.Targets.png"), plot = last_plot())
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ggsave(paste0(PLOT_loc, "/", Today, ".FeaturePlot.Targets.ps"), plot = last_plot())
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FeaturePlot(scRNAseqData, features = c(TARGET_A_alt), cols =  c("#ECECEC", "#DB003F"))

FeaturePlot(scRNAseqData, features = c(TARGET_B), cols =  c("#ECECEC", "#DB003F"))

# FeaturePlot(scRNAseqData, features = c(TARGET_C), cols =  c("#ECECEC", "#DB003F"))
# FeaturePlot(scRNAseqData, features = c(TARGET_D), cols =  c("#ECECEC", "#DB003F"))

4.3.2 Differential expression between cell communities

4.3.2.1 Macrophages

Comparison between the macrophages cell communities (CD14/CD68+), and all other communities.

N_GENES=20552
MAC.markers <- FindMarkers(object = scRNAseqData, 
                          ident.1 = c("CD14+CD68+ M I", 
                                      "CD14+CD68+ M II", 
                                      "CD14+CD68+ M III"), 
                          ident.2 = c(#"CD14+CD68+ M I", 
                                      #"CD14+CD68+ M II", 
                                      #"CD14+CD68+ M III",
                                      "CD3+CD8+ T I",
                                      "CD3+CD8A+ T II ", 
                                      "CD3+CD8 T III", 
                                      "CD3+CD4+ T I", 
                                      "CD3+CD4+ T II", 
                                      "CD3+CD4+ T III", 
                                      "CD34+ EC I", "CD34+ EC II",
                                      "Mixed I", 
                                      "Mixed II", 
                                      "ACTA2+ SMC", 
                                      "NCAM1+ NK", 
                                      "KIT+ MC",
                                      "CD79A+ B I", 
                                      "CD79A+ B II"))

  |                                                  | 0 % ~calculating  
  |+                                                 | 1 % ~29s          
  |++                                                | 2 % ~30s          
  |++                                                | 3 % ~29s          
  |+++                                               | 4 % ~01m 13s      
  |+++                                               | 5 % ~01m 04s      
  |++++                                              | 6 % ~57s          
  |++++                                              | 7 % ~53s          
  |+++++                                             | 8 % ~49s          
  |+++++                                             | 9 % ~47s          
  |++++++                                            | 10% ~45s          
  |++++++                                            | 11% ~42s          
  |+++++++                                           | 12% ~41s          
  |+++++++                                           | 13% ~39s          
  |++++++++                                          | 14% ~38s          
  |++++++++                                          | 15% ~37s          
  |+++++++++                                         | 16% ~36s          
  |+++++++++                                         | 17% ~35s          
  |++++++++++                                        | 18% ~35s          
  |++++++++++                                        | 19% ~34s          
  |+++++++++++                                       | 20% ~34s          
  |+++++++++++                                       | 21% ~33s          
  |++++++++++++                                      | 22% ~33s          
  |++++++++++++                                      | 23% ~32s          
  |+++++++++++++                                     | 24% ~32s          
  |+++++++++++++                                     | 26% ~31s          
  |++++++++++++++                                    | 27% ~31s          
  |++++++++++++++                                    | 28% ~31s          
  |+++++++++++++++                                   | 29% ~30s          
  |+++++++++++++++                                   | 30% ~30s          
  |++++++++++++++++                                  | 31% ~29s          
  |++++++++++++++++                                  | 32% ~28s          
  |+++++++++++++++++                                 | 33% ~27s          
  |+++++++++++++++++                                 | 34% ~27s          
  |++++++++++++++++++                                | 35% ~26s          
  |++++++++++++++++++                                | 36% ~26s          
  |+++++++++++++++++++                               | 37% ~25s          
  |+++++++++++++++++++                               | 38% ~24s          
  |++++++++++++++++++++                              | 39% ~24s          
  |++++++++++++++++++++                              | 40% ~23s          
  |+++++++++++++++++++++                             | 41% ~23s          
  |+++++++++++++++++++++                             | 42% ~22s          
  |++++++++++++++++++++++                            | 43% ~22s          
  |++++++++++++++++++++++                            | 44% ~21s          
  |+++++++++++++++++++++++                           | 45% ~21s          
  |+++++++++++++++++++++++                           | 46% ~20s          
  |++++++++++++++++++++++++                          | 47% ~20s          
  |++++++++++++++++++++++++                          | 48% ~20s          
  |+++++++++++++++++++++++++                         | 49% ~19s          
  |+++++++++++++++++++++++++                         | 50% ~19s          
  |++++++++++++++++++++++++++                        | 51% ~18s          
  |+++++++++++++++++++++++++++                       | 52% ~18s          
  |+++++++++++++++++++++++++++                       | 53% ~17s          
  |++++++++++++++++++++++++++++                      | 54% ~17s          
  |++++++++++++++++++++++++++++                      | 55% ~16s          
  |+++++++++++++++++++++++++++++                     | 56% ~16s          
  |+++++++++++++++++++++++++++++                     | 57% ~16s          
  |++++++++++++++++++++++++++++++                    | 58% ~15s          
  |++++++++++++++++++++++++++++++                    | 59% ~15s          
  |+++++++++++++++++++++++++++++++                   | 60% ~14s          
  |+++++++++++++++++++++++++++++++                   | 61% ~14s          
  |++++++++++++++++++++++++++++++++                  | 62% ~14s          
  |++++++++++++++++++++++++++++++++                  | 63% ~13s          
  |+++++++++++++++++++++++++++++++++                 | 64% ~13s          
  |+++++++++++++++++++++++++++++++++                 | 65% ~12s          
  |++++++++++++++++++++++++++++++++++                | 66% ~12s          
  |++++++++++++++++++++++++++++++++++                | 67% ~12s          
  |+++++++++++++++++++++++++++++++++++               | 68% ~11s          
  |+++++++++++++++++++++++++++++++++++               | 69% ~11s          
  |++++++++++++++++++++++++++++++++++++              | 70% ~11s          
  |++++++++++++++++++++++++++++++++++++              | 71% ~10s          
  |+++++++++++++++++++++++++++++++++++++             | 72% ~10s          
  |+++++++++++++++++++++++++++++++++++++             | 73% ~10s          
  |++++++++++++++++++++++++++++++++++++++            | 74% ~09s          
  |++++++++++++++++++++++++++++++++++++++            | 76% ~09s          
  |+++++++++++++++++++++++++++++++++++++++           | 77% ~08s          
  |+++++++++++++++++++++++++++++++++++++++           | 78% ~08s          
  |++++++++++++++++++++++++++++++++++++++++          | 79% ~08s          
  |++++++++++++++++++++++++++++++++++++++++          | 80% ~07s          
  |+++++++++++++++++++++++++++++++++++++++++         | 81% ~07s          
  |+++++++++++++++++++++++++++++++++++++++++         | 82% ~07s          
  |++++++++++++++++++++++++++++++++++++++++++        | 83% ~06s          
  |++++++++++++++++++++++++++++++++++++++++++        | 84% ~06s          
  |+++++++++++++++++++++++++++++++++++++++++++       | 85% ~05s          
  |+++++++++++++++++++++++++++++++++++++++++++       | 86% ~05s          
  |++++++++++++++++++++++++++++++++++++++++++++      | 87% ~05s          
  |++++++++++++++++++++++++++++++++++++++++++++      | 88% ~04s          
  |+++++++++++++++++++++++++++++++++++++++++++++     | 89% ~04s          
  |+++++++++++++++++++++++++++++++++++++++++++++     | 90% ~04s          
  |++++++++++++++++++++++++++++++++++++++++++++++    | 91% ~03s          
  |++++++++++++++++++++++++++++++++++++++++++++++    | 92% ~03s          
  |+++++++++++++++++++++++++++++++++++++++++++++++   | 93% ~03s          
  |+++++++++++++++++++++++++++++++++++++++++++++++   | 94% ~02s          
  |++++++++++++++++++++++++++++++++++++++++++++++++  | 95% ~02s          
  |++++++++++++++++++++++++++++++++++++++++++++++++  | 96% ~01s          
  |+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~01s          
  |+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~01s          
  |++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s          
  |++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=35s  
DT::datatable(MAC.markers)
MAC_Volcano_TargetsA = EnhancedVolcano(MAC.markers,
    lab = rownames(MAC.markers),
    x = "avg_log2FC",
    y = "p_val_adj",
    selectLab = GenesOfInterest_TargetsA,
    axisLabSize = 12,
    xlab = "average fold-change",
    title = "Macrophage markers\n(Macrophage communities vs the rest)",
    titleLabSize = 14,
    pCutoff = 0.05/N_GENES, # 20552 genes
    FCcutoff = 1.25,
    pointSize = 1.5,
    labSize = 3.0,
    legendLabels = c('NS','avg. fold-change','P',
      'P & avg. fold-change'),
    legendPosition = "right",
    legendLabSize = 10,
    legendIconSize = 3.0,
    drawConnectors = TRUE,
    widthConnectors = 0.2,
    colConnectors = "#595A5C",
    gridlines.major = FALSE,
    gridlines.minor = FALSE)
One or more p-values is 0. Converting to 10^-1 * current lowest non-zero p-value...
MAC_Volcano_TargetsA
ggsave(paste0(PLOT_loc, "/", Today, ".Volcano.MAC.DEG.Targets.pdf"), 
       plot = MAC_Volcano_TargetsA)
Saving 7.29 x 4.51 in image

On average FTL, LYZ, HLA-DRA, IFI30, and TCSB among others have a higher expression in the macrophage cell communities, than in all the others.

Below the results for the TARGET_GENES.

# temp = subset(MAC.markers, (rownames(MAC.markers)=="CCL2" | rownames(MAC.markers)=="CCR2" | rownames(MAC.markers)=="IL6" | rownames(MAC.markers)=="IL6R"))
temp = subset(MAC.markers, (rownames(MAC.markers)=="CCL2" | rownames(MAC.markers)=="CCR2"))
DT::datatable(temp)
rm(temp)

We will save these results too,


MAC.markers <- data.frame(Gene = rownames(MAC.markers), MAC.markers)
head(MAC.markers)

fwrite(MAC.markers, file = paste0(OUT_loc,"/",Today,".MAC.markers.txt.gz"),
       quote = FALSE, sep = "\t", na ="", dec = ".",
       showProgress = TRUE, verbose = FALSE,
       compress = c("gzip"))

5 Session information


Version:      v1.0.1
Last update:  2021-02-09
Written by:   Sander W. van der Laan (s.w.vanderlaan-2[at]umcutrecht.nl).
Description:  Script to load single-cell RNA sequencing (scRNAseq) data, and perform quality control (QC), and initial mapping to cells.
Minimum requirements: R version 3.5.2 (2018-12-20) -- 'Eggshell Igloo', macOS Mojave (10.14.2).

Change log
* v1.0.1 Update to reviewer comments and changes in EnhancedVolcano.
* v1.0.0 Initial version

sessionInfo()
R version 4.0.3 (2020-10-10)
Platform: x86_64-apple-darwin17.0 (64-bit)
Running under: macOS Big Sur 10.16

Matrix products: default
LAPACK: /Library/Frameworks/R.framework/Versions/4.0/Resources/lib/libRlapack.dylib

locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8

attached base packages:
 [1] parallel  stats4    tools     stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
 [1] RColorBrewer_1.1-2         SeuratObject_4.0.0         Seurat_4.0.0               EnhancedVolcano_1.8.0      ggrepel_0.9.1              mygene_1.26.0             
 [7] GenomicFeatures_1.42.1     GenomicRanges_1.42.0       GenomeInfoDb_1.26.2        org.Hs.eg.db_3.12.0        AnnotationDbi_1.52.0       IRanges_2.24.1            
[13] S4Vectors_0.28.1           Biobase_2.50.0             BiocGenerics_0.36.0        tidylog_1.0.2              ggsci_2.9                  GGally_2.1.0              
[19] PerformanceAnalytics_2.0.4 xts_0.12.1                 zoo_1.8-8                  ggcorrplot_0.1.3.999       Hmisc_4.4-2                Formula_1.2-4             
[25] lattice_0.20-41            survminer_0.4.8            survival_3.2-7             patchwork_1.1.1            openxlsx_4.2.3             ggpubr_0.4.0              
[31] tableone_0.12.0            labelled_2.7.0             sjPlot_2.8.7               sjlabelled_1.1.7           haven_2.3.1                devtools_2.3.2            
[37] usethis_2.0.0              MASS_7.3-53                DT_0.17                    knitr_1.31                 forcats_0.5.1              stringr_1.4.0             
[43] purrr_0.3.4                tibble_3.0.6               ggplot2_3.3.3              tidyverse_1.3.0            data.table_1.13.6          naniar_0.6.0              
[49] tidyr_1.1.2                dplyr_1.0.4                optparse_1.6.6             readr_1.4.0               

loaded via a namespace (and not attached):
  [1] ica_1.0-2                   Rsamtools_2.6.0             class_7.3-18                ps_1.5.0                    lmtest_0.9-38               rprojroot_2.0.2            
  [7] crayon_1.4.1                nlme_3.1-152                backports_1.2.1             reprex_1.0.0                rlang_0.4.10                XVector_0.30.0             
 [13] ROCR_1.0-11                 readxl_1.3.1                performance_0.7.0           irlba_2.3.3                 nloptr_1.2.2.2              extrafontdb_1.0            
 [19] callr_3.5.1                 proto_1.0.0                 BiocParallel_1.24.1         extrafont_0.17              bit64_4.0.5                 glue_1.4.2                 
 [25] sctransform_0.3.2           processx_3.4.5              vipor_0.4.5                 SummarizedExperiment_1.20.0 tidyselect_1.1.0            km.ci_0.5-2                
 [31] rio_0.5.16                  fitdistrplus_1.1-3          XML_3.99-0.5                proj4_1.0-10.1              GenomicAlignments_1.26.0    sjmisc_2.8.6               
 [37] chron_2.3-56                xtable_1.8-4                magrittr_2.0.1              evaluate_0.14               cli_2.3.0                   zlibbioc_1.36.0            
 [43] rstudioapi_0.13             miniUI_0.1.1.1              rpart_4.1-15                tinytex_0.29                maps_3.3.0                  shiny_1.6.0                
 [49] xfun_0.20                   askpass_1.1                 parameters_0.11.0           pkgbuild_1.2.0              cluster_2.1.0               listenv_0.8.0              
 [55] Biostrings_2.58.0           png_0.1-7                   reshape_0.8.8               future_1.21.0               withr_2.4.1                 bitops_1.0-6               
 [61] plyr_1.8.6                  cellranger_1.1.0            e1071_1.7-4                 survey_4.0                  coda_0.19-4                 pillar_1.4.7               
 [67] cachem_1.0.3                multcomp_1.4-16             fs_1.5.0                    vctrs_0.3.6                 ellipsis_0.3.1              generics_0.1.0             
 [73] gsubfn_0.7                  foreign_0.8-81              beeswarm_0.2.3              munsell_0.5.0               DelayedArray_0.16.1         emmeans_1.5.4              
 [79] rtracklayer_1.50.0          fastmap_1.1.0               compiler_4.0.3              pkgload_1.1.0               abind_1.4-5                 httpuv_1.5.5               
 [85] sessioninfo_1.1.1           plotly_4.9.3                GenomeInfoDbData_1.2.4      gridExtra_2.3               deldir_0.2-9                later_1.1.0.1              
 [91] BiocFileCache_1.14.0        jsonlite_1.7.2              scales_1.1.1                pbapply_1.4-3               carData_3.0-4               estimability_1.3           
 [97] lazyeval_0.2.2              promises_1.1.1              spatstat_1.64-1             car_3.0-10                  latticeExtra_0.6-29         goftest_1.2-2              
[103] spatstat.utils_2.0-0        reticulate_1.18             effectsize_0.4.3            checkmate_2.0.0             rmarkdown_2.6               ash_1.0-15                 
[109] sandwich_3.0-0              cowplot_1.1.1               statmod_1.4.35              Rtsne_0.15                  uwot_0.1.10                 igraph_1.2.6               
[115] yaml_2.2.1                  htmltools_0.5.1.1           memoise_2.0.0               quadprog_1.5-8              viridisLite_0.3.0           digest_0.6.27              
[121] assertthat_0.2.1            mime_0.9                    rappdirs_0.3.3              Rttf2pt1_1.3.8              bayestestR_0.8.2            KMsurv_0.1-5               
[127] RSQLite_2.2.3               sqldf_0.4-11                future.apply_1.7.0          remotes_2.2.0               blob_1.2.1                  survMisc_0.5.5             
[133] splines_4.0.3               labeling_0.4.2              RCurl_1.98-1.2              broom_0.7.4                 hms_1.0.0                   modelr_0.1.8               
[139] colorspace_2.0-0            base64enc_0.1-3             ggbeeswarm_0.6.0            ggrastr_0.2.1               nnet_7.3-15                 Rcpp_1.0.6                 
[145] RANN_2.6.1                  mvtnorm_1.1-1               clisymbols_1.2.0            parallelly_1.23.0           R6_2.5.0                    grid_4.0.3                 
[151] ggridges_0.5.3              lifecycle_0.2.0             zip_2.1.1                   curl_4.3                    ggsignif_0.6.0              minqa_1.2.4                
[157] leiden_0.3.7                testthat_3.0.1              getopt_1.20.3               Matrix_1.3-2                desc_1.2.0                  RcppAnnoy_0.0.18           
[163] TH.data_1.0-10              htmlwidgets_1.5.3           polyclip_1.10-0             biomaRt_2.46.2              crosstalk_1.1.1             rvest_0.3.6                
[169] mgcv_1.8-33                 globals_0.14.0              openssl_1.4.3               insight_0.12.0              htmlTable_2.1.0             codetools_0.2-18           
[175] matrixStats_0.58.0          lubridate_1.7.9.2           prettyunits_1.1.1           dbplyr_2.1.0                gtable_0.3.0                DBI_1.1.1                  
[181] visdat_0.5.3                tensor_1.5                  httr_1.4.2                  KernSmooth_2.23-18          stringi_1.5.3               progress_1.2.2             
[187] reshape2_1.4.4              farver_2.0.3                xml2_1.3.2                  boot_1.3-26                 ggeffects_1.0.1             ggalt_0.4.0                
[193] lme4_1.1-26                 scattermore_0.7             bit_4.0.4                   MatrixGenerics_1.2.1        sjstats_0.18.1              jpeg_0.1-8.1               
[199] spatstat.data_1.7-0         pkgconfig_2.0.3             rstatix_0.6.0               mitools_2.4                

6 Saving environment

save.image(paste0(PROJECT_loc, "/",Today,".",PROJECTNAME,".scrnaseq_results.RData"))
© 1979-2021 Sander W. van der Laan | s.w.vanderlaan-2[at]umcutrecht.nl | swvanderlaan.github.io.

  1. Georgakis MK, Gill D, Rannikmae K, Traylor M, Anderson CD, Lee JM, Kamatani Y, Hopewell JC, Worrall BB, Bernhagen J, Sudlow CLM, Malik R, Dichgans M. Genetically determined levels of circulating cytokines and risk of stroke. Circulation. 2019;139:256-268↩︎

  2. Georgakis MK, Gill D, Rannikmae K, Traylor M, Anderson CD, Lee JM, Kamatani Y, Hopewell JC, Worrall BB, Bernhagen J, Sudlow CLM, Malik R, Dichgans M. Genetically determined levels of circulating cytokines and risk of stroke. Circulation. 2019;139:256-268↩︎

  3. Georgakis MK, Malik R, Bjorkbacka H, Pana TA, Demissie S, Ayers C, Elhadad MA, Fornage M, Beiser AS, Benjamin EJ, Boekholdt MS, Engstrom G, Herder C, Hoogeveen RC, Koenig W, Melander O, Orho-Melander M, Schiopu A, Soderholm M, Wareham N, Ballantyne CM, Peters A, Seshadri S, Myint PK, Nilsson J, de Lemos JA, Dichgans M. Circulating monocyte chemoattractant protein-1 and risk of stroke: Meta-analysis of population-based studies involving 17 180 individuals. Circ Res. 2019;125:773-782↩︎

  4. Nelken NA, Coughlin SR, Gordon D, Wilcox JN. Monocyte chemoattractant protein-1 in human atheromatous plaques. J Clin Invest. 1991;88:1121-1127↩︎

  5. Papadopoulou C, Corrigall V, Taylor PR, Poston RN. The role of the chemokines MCP-1, GRO-alpha, IL-8 and their receptors in the adhesion of monocytic cells to human atherosclerotic plaques. Cytokine. 2008;43:181-186↩︎

  6. Takeya M, Yoshimura T, Leonard EJ, Takahashi K. Detection of monocyte chemoattractant protein-1 in human atherosclerotic lesions by an anti-monocyte chemoattractant protein-1 monoclonal antibody. Hum Pathol. 1993;24:534-539↩︎

  7. Wilcox JN, Nelken NA, Coughlin SR, Gordon D, Schall TJ. Local expression of inflammatory cytokines in human atherosclerotic plaques. J Atheroscler Thromb. 1994;1 Suppl 1:S10-13↩︎

---
title: "Mapping MCP1 (CCL2) with single-cell resolution in carotid plaques."
author: '[Sander W. van der Laan, PhD](https://swvanderlaan.github.io) | @swvanderlaan'
date: '`r Sys.Date()`'
output:
  html_notebook: 
    cache: yes
    code_folding: hide
    collapse: yes
    df_print: paged
    fig.align: center
    fig_caption: yes
    fig_height: 10
    fig_retina: 2
    fig_width: 12
    number_sections: yes
    theme: paper
    toc: yes
    toc_float:
      collapsed: no
      smooth_scroll: yes
mainfont: Helvetica
subtitle: A 'druggable-MI-targets' project
editor_options:
  chunk_output_type: inline
---
```{r global_options, include=FALSE}
# further define some knitr-options.
knitr::opts_chunk$set(fig.width = 12, fig.height = 8, fig.path = 'Figures/',
                      eval = TRUE, warning = FALSE, message = FALSE)
```

_Clean the environment._
```{r ClearEnvironment, echo = FALSE}
# rm(list = ls())
```

_Set locations, and the working directory ..._
```{r LocalSystem, echo = FALSE}
### Operating System Version
### Mac Pro
# ROOT_loc = "/Volumes/EliteProQx2Media"
# GENOMIC_loc = "/Users/svanderlaan/iCloud/Genomics"

### MacBook Pro
# ROOT_loc = "/Users/swvanderlaan"
# GENOMIC_loc = paste0(ROOT_loc, "/iCloud/Genomics")

### MacBook Air
ROOT_loc = "/Users/slaan3"
GENOMIC_loc = paste0(ROOT_loc, "/iCloud/Genomics")

### Generic Locations
AEDB_loc = paste0(GENOMIC_loc, "/AE-AAA_GS_DBs")
LAB_loc = paste0(GENOMIC_loc, "/LabBusiness")
RESULTS = paste0(ROOT_loc, "/PLINK/analyses/lookups/AE_20190912_010_MDICHGANS_SWVDLAAN_IL6_MCP1/scRNAseq")
RAWDATA = paste0(ROOT_loc, "/PLINK/_AE_ORIGINALS/AESCRNA/prepped_data")
PROJECT_loc = paste0(ROOT_loc, "/PLINK/analyses/lookups/AE_20190912_010_MDICHGANS_SWVDLAAN_IL6_MCP1/scRNAseq")

### SOME VARIABLES WE NEED DOWN THE LINE
cat("\nDefining phenotypes and datasets.\n")
PROJECTNAME="AESCRNA"
TARGET_GENES="MCP1, CCR2, IL6, IL6R"
TARGET_A="MCP1"
TARGET_A_alt="CCL2"
TARGET_B="CCR2"
TARGET_C="IL6"
TARGET_D="IL6R"

cat("\nCreate a new analysis directory, including subdirectories.\n")
# Analysis
ifelse(!dir.exists(file.path(PROJECT_loc, "/",PROJECTNAME)), 
       dir.create(file.path(PROJECT_loc, "/",PROJECTNAME)), 
       FALSE)
ANALYSIS_loc = paste0(PROJECT_loc,"/",PROJECTNAME)

# Plots
ifelse(!dir.exists(file.path(ANALYSIS_loc, "/PLOTS")), 
       dir.create(file.path(ANALYSIS_loc, "/PLOTS")), 
       FALSE)
PLOT_loc = paste0(ANALYSIS_loc,"/PLOTS")

# QC plots
ifelse(!dir.exists(file.path(PLOT_loc, "/QC")), 
       dir.create(file.path(PLOT_loc, "/QC")), 
       FALSE)
QC_loc = paste0(PLOT_loc,"/QC")

# Output files
ifelse(!dir.exists(file.path(ANALYSIS_loc, "/OUTPUT")), 
       dir.create(file.path(ANALYSIS_loc, "/OUTPUT")), 
       FALSE)
OUT_loc = paste0(ANALYSIS_loc, "/OUTPUT")

cat("\nSetting working directory and listing its contents.\n")
setwd(paste0(PROJECT_loc))
getwd()
list.files()
```

_... a package-installation function ..._
```{r Function: installations, echo=FALSE}
install.packages.auto <- function(x) { 
  x <- as.character(substitute(x)) 
  if(isTRUE(x %in% .packages(all.available = TRUE))) { 
    eval(parse(text = sprintf("require(\"%s\")", x)))
  } else { 
    # Update installed packages - this may mean a full upgrade of R, which in turn
    # may not be warrented. 
    # update.install.packages.auto(ask = FALSE) 
    eval(parse(text = sprintf("install.packages(\"%s\", dependencies = TRUE, repos = \"https://cloud.r-project.org/\")", x)))
  }
  if(isTRUE(x %in% .packages(all.available = TRUE))) { 
    eval(parse(text = sprintf("require(\"%s\")", x)))
  } else {
    if (!requireNamespace("BiocManager"))
      install.packages("BiocManager")
    # BiocManager::install() # this would entail updating installed packages, which in turned may not be warrented
    eval(parse(text = sprintf("BiocManager::install(\"%s\")", x)))
    eval(parse(text = sprintf("require(\"%s\")", x)))
  }
}
```

_... and load those packages._
```{r Setting: loading_packages, echo=FALSE, message=FALSE, warning=FALSE}
install.packages.auto("readr")
install.packages.auto("optparse")
install.packages.auto("tools")
install.packages.auto("dplyr")
install.packages.auto("tidyr")
install.packages.auto("tidylog")
library("tidylog", warn.conflicts = FALSE)
install.packages.auto("naniar")

# To get 'data.table' with 'fwrite' to be able to directly write gzipped-files
# Ref: https://stackoverflow.com/questions/42788401/is-possible-to-use-fwrite-from-data-table-with-gzfile
# install.packages("data.table", repos = "https://Rdatatable.gitlab.io/data.table")
library(data.table)

install.packages.auto("tidyverse")
install.packages.auto("knitr")
install.packages.auto("DT")

install.packages.auto("org.Hs.eg.db")
install.packages.auto("mygene")
install.packages.auto("EnhancedVolcano")

install.packages.auto("haven")
install.packages.auto("tableone")

# install.packages.auto("Seurat") # latest version

# Install the devtools package from Hadley Wickham
install.packages.auto('devtools')
# Replace '2.3.4' with your desired version
# devtools::install_version(package = 'Seurat', version = package_version('2.3.4'))
library("Seurat")


```

_We will create a datestamp and define the Utrecht Science Park Colour Scheme_.
```{r Setting: Colors, echo=FALSE}

# Today = format(as.Date(as.POSIXlt(Sys.time())), "%Y%m%d")
# Today.Report = format(as.Date(as.POSIXlt(Sys.time())), "%A, %B %d, %Y")

### UtrechtScienceParkColoursScheme
###
### WebsitetoconvertHEXtoRGB:http://hex.colorrrs.com.
### Forsomefunctionsyoushoulddividethesenumbersby255.
### 
###	No.	Color			      HEX	(RGB)						              CHR		  MAF/INFO
###---------------------------------------------------------------------------------------
###	1	  yellow			    #FBB820 (251,184,32)				      =>	1		or 1.0>INFO
###	2	  gold			      #F59D10 (245,157,16)				      =>	2		
###	3	  salmon			    #E55738 (229,87,56)				      =>	3		or 0.05<MAF<0.2 or 0.4<INFO<0.6
###	4	  darkpink		    #DB003F ((219,0,63)				      =>	4		
###	5	  lightpink		    #E35493 (227,84,147)				      =>	5		or 0.8<INFO<1.0
###	6	  pink			      #D5267B (213,38,123)				      =>	6		
###	7	  hardpink		    #CC0071 (204,0,113)				      =>	7		
###	8	  lightpurple	    #A8448A (168,68,138)				      =>	8		
###	9	  purple			    #9A3480 (154,52,128)				      =>	9		
###	10	lavendel		    #8D5B9A (141,91,154)				      =>	10		
###	11	bluepurple		  #705296 (112,82,150)				      =>	11		
###	12	purpleblue		  #686AA9 (104,106,169)			      =>	12		
###	13	lightpurpleblue	#6173AD (97,115,173/101,120,180)	=>	13		
###	14	seablue			    #4C81BF (76,129,191)				      =>	14		
###	15	skyblue			    #2F8BC9 (47,139,201)				      =>	15		
###	16	azurblue		    #1290D9 (18,144,217)				      =>	16		or 0.01<MAF<0.05 or 0.2<INFO<0.4
###	17	lightazurblue	  #1396D8 (19,150,216)				      =>	17		
###	18	greenblue		    #15A6C1 (21,166,193)				      =>	18		
###	19	seaweedgreen	  #5EB17F (94,177,127)				      =>	19		
###	20	yellowgreen		  #86B833 (134,184,51)				      =>	20		
###	21	lightmossgreen	#C5D220 (197,210,32)				      =>	21		
###	22	mossgreen		    #9FC228 (159,194,40)				      =>	22		or MAF>0.20 or 0.6<INFO<0.8
###	23	lightgreen	  	#78B113 (120,177,19)				      =>	23/X
###	24	green			      #49A01D (73,160,29)				      =>	24/Y
###	25	grey			      #595A5C (89,90,92)				        =>	25/XY	or MAF<0.01 or 0.0<INFO<0.2
###	26	lightgrey		    #A2A3A4	(162,163,164)			      =>	26/MT
###
###	ADDITIONAL COLORS
###	27	midgrey			#D7D8D7
###	28	verylightgrey	#ECECEC"
###	29	white			#FFFFFF
###	30	black			#000000
###----------------------------------------------------------------------------------------------

uithof_color = c("#FBB820","#F59D10","#E55738","#DB003F","#E35493","#D5267B",
                 "#CC0071","#A8448A","#9A3480","#8D5B9A","#705296","#686AA9",
                 "#6173AD","#4C81BF","#2F8BC9","#1290D9","#1396D8","#15A6C1",
                 "#5EB17F","#86B833","#C5D220","#9FC228","#78B113","#49A01D",
                 "#595A5C","#A2A3A4", "#D7D8D7", "#ECECEC", "#FFFFFF", "#000000")

uithof_color_legend = c("#FBB820", "#F59D10", "#E55738", "#DB003F", "#E35493",
                        "#D5267B", "#CC0071", "#A8448A", "#9A3480", "#8D5B9A",
                        "#705296", "#686AA9", "#6173AD", "#4C81BF", "#2F8BC9",
                        "#1290D9", "#1396D8", "#15A6C1", "#5EB17F", "#86B833",
                        "#C5D220", "#9FC228", "#78B113", "#49A01D", "#595A5C",
                        "#A2A3A4", "#D7D8D7", "#ECECEC", "#FFFFFF", "#000000")

#ggplot2 default color palette
gg_color_hue <- function(n) {
  hues = seq(15, 375, length = n + 1)
  hcl(h = hues, l = 65, c = 100)[1:n]
}

### ----------------------------------------------------------------------------
```

# ERA-CVD 'druggable-MI-targets'
<!-- ![ERA-CVD logo]("Users/swvanderlaan/iCloud/Genomics/Projects/#Druggable-MI-Genes/Administration/ERA-CVD\ Logo_CMYK.jpg") -->

For the ERA-CVD 'druggable-MI-targets' project (grantnumber: 01KL1802) we will perform two related RNA sequencing (RNAseq) experiments:

1) conventional ('bulk') RNAseq using RNA extracted from carotid plaque samples, n ± 700. As of `r Today.Report` all samples have been selected and RNA has been extracted; quality control (QC) was performed and we have a dataset of 635 samples.

2) single-cell RNAseq (scRNAseq) of at least n = 40 samples (20 females, 20 males). As of `r Today.Report` data is available of 40 samples (3 females, 15 males), we are extending sampling to get more female samples.

Plaque samples are derived from carotid endarterectomies as part of the [Athero-Express Biobank Study](http:www/atheroexpress.nl) which is an ongoing study in the UMC Utrecht.


# Background

Using a Mendelian Randomization approach, we recently examined associations between the circulating levels of 41 cytokines and growth factors and the risk of stroke in the MEGASTROKE GWAS dataset (67,000 stroke cases and 450,000 controls) and found `TARGET_A` as the cytokine showing the strongest association with stroke, particularly large artery and cardioembolic stroke[^1]. Genetically elevated MCP-1 levels were also associated with a higher risk of coronary artery disease (CAD) and myocardial infarction (MI)[^1]. Further, in a meta-analysis of observational population-based of longitudinal cohort studies we recently showed that baseline levels of `TARGET_A` were associated with a higher risk of ischemic stroke over follow-up[^2]. 
`TARGET_A`

While these data suggest a central role of `TARGET_A` in the pathogenesis of atherosclerosis, it remains unknown if `TARGET_A` levels in the blood really reflect `TARGET_A` activity. `TARGET_A` is expressed in the atherosclerotic plaque and attracts monocytes in the subendothelial space[^3][^4][^5][^6]. Thus, `TARGET_A` levels in the plaque might more strongly reflect `TARGET_A` signaling. However, it remains unknown which cells in the plaques are interacting with the circulating monocytes.

In this project we aim to map these genes to individual cells from carotid endarterectomy patients. 


[^1]: Georgakis MK, Gill D, Rannikmae K, Traylor M, Anderson CD, Lee JM, Kamatani Y, Hopewell JC, Worrall BB, Bernhagen J, Sudlow CLM, Malik R, Dichgans M. **Genetically determined levels of circulating cytokines and risk of stroke.** _Circulation._ 2019;139:256-268
[^2]: Georgakis MK, Malik R, Bjorkbacka H, Pana TA, Demissie S, Ayers C, Elhadad MA, Fornage M, Beiser AS, Benjamin EJ, Boekholdt MS, Engstrom G, Herder C, Hoogeveen RC, Koenig W, Melander O, Orho-Melander M, Schiopu A, Soderholm M, Wareham N, Ballantyne CM, Peters A, Seshadri S, Myint PK, Nilsson J, de Lemos JA, Dichgans M. **Circulating monocyte chemoattractant protein-1 and risk of stroke: Meta-analysis of population-based studies involving 17 180 individuals.** _Circ Res._ 2019;125:773-782
[^3]: Nelken NA, Coughlin SR, Gordon D, Wilcox JN. **Monocyte chemoattractant protein-1 in human atheromatous plaques.** _J Clin Invest._ 1991;88:1121-1127
[^4]: Papadopoulou C, Corrigall V, Taylor PR, Poston RN. **The role of the chemokines MCP-1, GRO-alpha, IL-8 and their receptors in the adhesion of monocytic cells to human atherosclerotic plaques.** _Cytokine._ 2008;43:181-186
[^5]: Takeya M, Yoshimura T, Leonard EJ, Takahashi K. **Detection of monocyte chemoattractant protein-1 in human atherosclerotic lesions by an anti-monocyte chemoattractant protein-1 monoclonal antibody.** _Hum Pathol._ 1993;24:534-539
[^6]: Wilcox JN, Nelken NA, Coughlin SR, Gordon D, Schall TJ. **Local expression of inflammatory cytokines in human atherosclerotic plaques.** _J Atheroscler Thromb._ 1994;1 Suppl 1:S10-13


# Load data
First we will load the data:

- scRNAseq experimental data and rename the cell types.
- Athero-Express clinical data.

## AESCRNA: single-cell RNAseq from carotid plaques

Here we load the latest dataset from our Athero-Express Single Cell RNA experiment.

```{r LoadData}

scRNAseqData <- readRDS(paste0(RAWDATA, "/Seuset_40_patients/Seuset_40_patients.RDS"))
scRNAseqData
N_GENES=18283

```

The naming/classification is based on a combination conventional markers. We do not claim to know the exact identity of each cell, rather we refer to cells as 'KIT+ Mast cells"-like cells. Likewise we refer to the cell clusters as 'communities' of cells that exihibit similar properties, _i.e._ similar defining markers (_e.g. KIT_). 

We will rename the cell types to human readable names. 
```{r Change cell cummunity names}
### change names for clarity
backup.scRNAseqData = scRNAseqData
# get the old names to change to new names
UMAPPlot(scRNAseqData, label = FALSE, pt.size = 1.25, label.size = 4, group.by = "ident")

unique(scRNAseqData@active.ident)

celltypes <- c("CD14+CD68+ Macrophages I" = "CD14+CD68+ M I", 
               "CD14+CD68+ Macrophages II" = "CD14+CD68+ M II", 
               "CD14+CD68+ Macrophages III" = "CD14+CD68+ M III",
               "CD3+CD8+ T cells I" = "CD3+CD8+ T I",
               "CD3+CD8A+ T Cells II" = "CD3+CD8A+ T II ", 
               "CD3+CD8 T cells III" = "CD3+CD8 T III", 
               "CD3+CD4+ T Cells I" = "CD3+CD4+ T I", 
               "CD3+CD4+ T Cells II" = "CD3+CD4+ T II", 
               "CD3+CD4+ T Cells III" = "CD3+CD4+ T III", 
               "CD34+ Endothelial Cells I" = "CD34+ EC I", 
               "CD34+ Endothelial Cells II" = "CD34+ EC II", 
               "Mixed Cells I" = "Mixed I", 
               "Mixed Cells II" = "Mixed II", 
               "ACTA2+ Smooth Muscle Cells" = "ACTA2+ SMC", 
               "NCAM1+ Natural Killer Cells" = "NCAM1+ NK", 
               "KIT+ Mast Cells" = "KIT+ MC",
               "CD79A+ B Cells I" = "CD79A+ B I", 
               "CD79A+ B Cells II" = "CD79A+ B II")

scRNAseqData <- Seurat::RenameIdents(object = scRNAseqData, 
                                       celltypes)
```

```{r Change cell cummunity names - new plot}
UMAPPlot(scRNAseqData, label = TRUE, pt.size = 1.25, label.size = 4, group.by = "ident",
         repel = TRUE)

```

## Athero-Express Biobank Study: clinical data

Loading Athero-Express clinical data.
```{r LoadAEDB}
require(haven)

# AEDB <- haven::read_sav(paste0(AEDB_loc, "/2019-3NEW_AtheroExpressDatabase_ScientificAE_02072019_IC_added.sav"))
AEDB <- haven::read_sav(paste0(AEDB_loc, "/2020_1_NEW_AtheroExpressDatabase_ScientificAE_16-03-2020.sav"))

```

### Fix clinical data

We need to be very strict in defining _symptoms._ Therefore we will fix a new variable that groups _symptoms_ at inclusion.

Coding of _symptoms_ is as follows:

- missing	-999	
- Asymptomatic	0	
- TIA	1	
- minor stroke	2	
- Major stroke	3	
- Amaurosis fugax	4	
- Four vessel disease	5	
- Vertebrobasilary TIA	7	
- Retinal infarction	8	
- Symptomatic, but aspecific symtoms	9
- Contralateral symptomatic occlusion	10	
- retinal infarction	11	
- armclaudication due to occlusion subclavian artery, CEA needed for bypass	12	
- retinal infarction + TIAs	13	
- Ocular ischemic syndrome	14	
- ischemisch glaucoom	15	
- subclavian steal syndrome	16	
- TGA	17

We will group as follows:

1. Asymptomatic > 0
2. TIA > 1, 7, 13
3. Stroke > 2, 3
4. Ocular > 4, 14, 15
5. Retinal infarction > 8, 11
6. Other > 5, 9, 10, 12, 16, 17


```{r FixSymptoms, message=FALSE, warning=FALSE}

# Fix symptoms

attach(AEDB)
AEDB[,"Symptoms.5G"] <- NA
AEDB$Symptoms.5G[sympt == 0] <- "Asymptomatic"
AEDB$Symptoms.5G[sympt == 1 | sympt == 7 | sympt == 13] <- "TIA"
AEDB$Symptoms.5G[sympt == 2 | sympt == 3] <- "Stroke"
AEDB$Symptoms.5G[sympt == 4 | sympt == 14 | sympt == 15 ] <- "Ocular"
AEDB$Symptoms.5G[sympt == 8 | sympt == 11] <- "Retinal infarction"
AEDB$Symptoms.5G[sympt == 5 | sympt == 9 | sympt == 10 | sympt == 12 | sympt == 16 | sympt == 17] <- "Other"


# AsymptSympt
AEDB[,"AsymptSympt"] <- NA
AEDB$AsymptSympt[sympt == -999] <- NA
AEDB$AsymptSympt[sympt == 0] <- "Asymptomatic"
AEDB$AsymptSympt[sympt == 1 | sympt == 7 | sympt == 13 | sympt == 2 | sympt == 3] <- "Symptomatic"
AEDB$AsymptSympt[sympt == 4 | sympt == 14 | sympt == 15 | sympt == 8 | sympt == 11 | sympt == 5 | sympt == 9 | sympt == 10 | sympt == 12 | sympt == 16 | sympt == 17] <- "Ocular and others"

# AsymptSympt
AEDB[,"AsymptSympt2G"] <- NA
AEDB$AsymptSympt2G[sympt == -999] <- NA
AEDB$AsymptSympt2G[sympt == 0] <- "Asymptomatic"
AEDB$AsymptSympt2G[sympt == 1 | sympt == 7 | sympt == 13 | sympt == 2 | sympt == 3 | sympt == 4 | sympt == 14 | sympt == 15 | sympt == 8 | sympt == 11 | sympt == 5 | sympt == 9 | sympt == 10 | sympt == 12 | sympt == 16 | sympt == 17] <- "Symptomatic"

detach(AEDB)

# table(AEDB$sympt, useNA = "ifany")
# table(AEDB$AsymptSympt2G, useNA = "ifany")
# table(AEDB$Symptoms.5G, useNA = "ifany")
# 
# table(AEDB$AsymptSympt2G, AEDB$sympt, useNA = "ifany")
# table(AEDB$Symptoms.5G, AEDB$sympt, useNA = "ifany")
table(AEDB$AsymptSympt2G, AEDB$Symptoms.5G, useNA = "ifany")

# AEDB.temp <- subset(AEDB,  select = c("STUDY_NUMBER", "UPID", "Age", "Gender", "Hospital", "Artery_summary", "sympt", "Symptoms.5G", "AsymptSympt"))
# require(labelled)
# AEDB.temp$Gender <- to_factor(AEDB.temp$Gender)
# AEDB.temp$Hospital <- to_factor(AEDB.temp$Hospital)
# AEDB.temp$Artery_summary <- to_factor(AEDB.temp$Artery_summary)
# 
# DT::datatable(AEDB.temp[1:10,], caption = "Excerpt of the whole AEDB.", rownames = FALSE)
# 
# table(AEDB.temp$Symptoms.5G, AEDB.temp$AsymptSympt)
# 
# rm(AEDB.temp)
```

We will also fix the _plaquephenotypes_ variable.  

Coding of symptoms is as follows:

- missing	-999	
- not relevant -888
- fibrous	1	
- fibroatheromatous	2	
- atheromatous	3	


```{r FixPlaquePhenotypes, message=FALSE, warning=FALSE}

# Fix plaquephenotypes
attach(AEDB)
AEDB[,"OverallPlaquePhenotype"] <- NA
AEDB$OverallPlaquePhenotype[plaquephenotype == -999] <- NA
AEDB$OverallPlaquePhenotype[plaquephenotype == -999] <- NA
AEDB$OverallPlaquePhenotype[plaquephenotype == 1] <- "fibrous"
AEDB$OverallPlaquePhenotype[plaquephenotype == 2] <- "fibroatheromatous"
AEDB$OverallPlaquePhenotype[plaquephenotype == 3] <- "atheromatous"
detach(AEDB)

# AEDB.temp <- subset(AEDB,  select = c("STUDY_NUMBER", "UPID", "Age", "Gender", "Hospital", "Artery_summary", "plaquephenotype", "OverallPlaquePhenotype"))
# require(labelled)
# AEDB.temp$Gender <- to_factor(AEDB.temp$Gender)
# AEDB.temp$Hospital <- to_factor(AEDB.temp$Hospital)
# AEDB.temp$Artery_summary <- to_factor(AEDB.temp$Artery_summary)
# 
# DT::datatable(AEDB.temp[1:10,], caption = "Excerpt of the whole AEDB.", rownames = FALSE)
# 
# rm(AEDB.temp)

```

We will also fix the _diabetes_ status variable.

```{r FixDiabetes, message=FALSE, warning=FALSE}

# Fix diabetes
attach(AEDB)
AEDB[,"DiabetesStatus"] <- NA
AEDB$DiabetesStatus[DM.composite == -999] <- NA
AEDB$DiabetesStatus[DM.composite == 0] <- "Control (no Diabetes Dx/Med)"
AEDB$DiabetesStatus[DM.composite == 1] <- "Diabetes"
detach(AEDB)

# AEDB.temp <- subset(AEDB,  select = c("STUDY_NUMBER", "UPID", "Age", "Gender", "Hospital", "Artery_summary", "DM.composite", "DiabetesStatus"))
# require(labelled)
# AEDB.temp$Gender <- to_factor(AEDB.temp$Gender)
# AEDB.temp$Hospital <- to_factor(AEDB.temp$Hospital)
# AEDB.temp$Artery_summary <- to_factor(AEDB.temp$Artery_summary)
# AEDB.temp$DiabetesStatus <- to_factor(AEDB.temp$DiabetesStatus)
# 
# DT::datatable(AEDB.temp[1:10,], caption = "Excerpt of the whole AEDB.", rownames = FALSE)
# 
# rm(AEDB.temp)

```


We will also fix the _smoking_ status variable. We are interested in whether someone never, ever or is currently (at the time of inclusion) smoking. This is based on the questionnaire. 

- `diet801`: are you a smoker?
- `diet802`: did you smoke in the past?

We already have some variables indicating smoking status:

- `SmokingReported`: patient has reported to smoke.
- `SmokingYearOR`: smoking in the year of surgery?
- `SmokerCurrent`: currently smoking?



```{r FixSmoking, message=FALSE, warning=FALSE}
require(labelled)
AEDB$diet801 <- to_factor(AEDB$diet801)
AEDB$diet802 <- to_factor(AEDB$diet802)
AEDB$diet805 <- to_factor(AEDB$diet805)
AEDB$SmokingReported <- to_factor(AEDB$SmokingReported)
AEDB$SmokerCurrent <- to_factor(AEDB$SmokerCurrent)
AEDB$SmokingYearOR <- to_factor(AEDB$SmokingYearOR)

# table(AEDB$diet801)
# table(AEDB$diet802)
# table(AEDB$SmokingReported)
# table(AEDB$SmokerCurrent)
# table(AEDB$SmokingYearOR)
# table(AEDB$SmokingReported, AEDB$SmokerCurrent, useNA = "ifany", dnn = c("Reported smoking", "Current smoker"))
# 
# table(AEDB$diet801, AEDB$diet802, useNA = "ifany", dnn = c("Smoker", "Past smoker"))

cat("\nFixing smoking status.\n")
attach(AEDB)
AEDB[,"SmokerStatus"] <- NA
AEDB$SmokerStatus[diet802 == "don't know"] <- "Never smoked"
AEDB$SmokerStatus[diet802 == "I still smoke"] <- "Current smoker"
AEDB$SmokerStatus[SmokerCurrent == "no" & diet802 == "no"] <- "Never smoked"
AEDB$SmokerStatus[SmokerCurrent == "no" & diet802 == "yes"] <- "Ex-smoker"
AEDB$SmokerStatus[SmokerCurrent == "yes"] <- "Current smoker"
AEDB$SmokerStatus[SmokerCurrent == "no data available/missing"] <- NA
# AEDB$SmokerStatus[is.na(SmokerCurrent)] <- "Never smoked"
detach(AEDB)

cat("\n* Current smoking status.\n")
table(AEDB$SmokerCurrent,
      useNA = "ifany", 
      dnn = c("Current smoker"))

cat("\n* Updated smoking status.\n")
table(AEDB$SmokerStatus,
      useNA = "ifany", 
      dnn = c("Updated smoking status"))

cat("\n* Comparing to 'SmokerCurrent'.\n")
table(AEDB$SmokerStatus, AEDB$SmokerCurrent, 
      useNA = "ifany", 
      dnn = c("Updated smoking status", "Current smoker"))

# AEDB.temp <- subset(AEDB,  select = c("STUDY_NUMBER", "UPID", "Age", "Gender", "Hospital", "Artery_summary", "DM.composite", "DiabetesStatus"))
# require(labelled)
# AEDB.temp$Gender <- to_factor(AEDB.temp$Gender)
# AEDB.temp$Hospital <- to_factor(AEDB.temp$Hospital)
# AEDB.temp$Artery_summary <- to_factor(AEDB.temp$Artery_summary)
# AEDB.temp$DiabetesStatus <- to_factor(AEDB.temp$DiabetesStatus)
# 
# DT::datatable(AEDB.temp[1:10,], caption = "Excerpt of the whole AEDB.", rownames = FALSE)
# 
# rm(AEDB.temp)


```

We will also fix the _alcohol_ status variable.


```{r FixAlcohol, message=FALSE, warning=FALSE}

# Fix diabetes
attach(AEDB)
AEDB[,"AlcoholUse"] <- NA
AEDB$AlcoholUse[diet810 == -999] <- NA
AEDB$AlcoholUse[diet810 == 0] <- "No"
AEDB$AlcoholUse[diet810 == 1] <- "Yes"
detach(AEDB)

# AEDB.temp <- subset(AEDB,  select = c("STUDY_NUMBER", "UPID", "Age", "Gender", "Hospital", "Artery_summary", "diet810", "AlcoholUse"))
# require(labelled)
# AEDB.temp$Gender <- to_factor(AEDB.temp$Gender)
# AEDB.temp$Hospital <- to_factor(AEDB.temp$Hospital)
# AEDB.temp$Artery_summary <- to_factor(AEDB.temp$Artery_summary)
# AEDB.temp$AlcoholUse <- to_factor(AEDB.temp$AlcoholUse)
# 
# DT::datatable(AEDB.temp[1:10,], caption = "Excerpt of the whole AEDB.", rownames = FALSE)
# 
# rm(AEDB.temp)


```

### Prepare baseline characteristics

We are interested in the following variables at baseline.

- Age (years)
- Female sex (N, %)
- Hypertension (N, %)
- SBP (mmHg)
- DBP (mmHg)
- Diabetes mellitus (N, %)
- Total cholesterol levels (mg/dL)
- LDL cholesterol levels (mg/dL)
- HDL cholesterol levels (mg/dL)
- Triglyceride levels (mg/dL)
- Use of statins (N, %)
- Use of antiplatelet drugs (N, %)
- BMI (kg/m²)
- Smoking status (N, %)
  - Never smokers
  - Ex-smokers
  - Current smokers
- History of CAD (N, %)
- History of PAD (N, %)
- Clinical manifestations
  - Asymptomatic
  - Amaurosis fugax
  - TIA
  - Stroke
- eGFR (mL/min/1.73 m²)
- MCP-1 plaque levels (pg/mL)

```{r Baseline AEDB: preparation}
cat("====================================================================================================\n")
cat("SELECTION THE SHIZZLE\n")

### Artery levels
# AEdata$Artery_summary: 
#           value                                                                                   label
# NOT USE - 0 No artery known (yet), no surgery (patient ill, died, exited study), re-numbered to AAA
# USE - 1                                                                  carotid (left & right)
# USE - 2                                               femoral/iliac (left, right or both sides)
# NOT USE - 3                                               other carotid arteries (common, external)
# NOT USE - 4                                   carotid bypass and injury (left, right or both sides)
# NOT USE - 5                                                         aneurysmata (carotid & femoral)
# NOT USE - 6                                                                                   aorta
# NOT USE - 7                                            other arteries (renal, popliteal, vertebral)
# NOT USE - 8                        femoral bypass, angioseal and injury (left, right or both sides)

### AEdata$informedconsent
#           value                                                                                           label
# NOT USE - -999                                                                                         missing
# NOT USE - 0                                                                                        no, died
# USE - 1                                                                                             yes
# USE - 2                                                             yes, health treatment when possible
# USE - 3                                                                        yes, no health treatment
# USE - 4                                                yes, no health treatment, no commercial business
# NOT USE - 5                                                          yes, no tissue, no commerical business
# NOT USE - 6                      yes, no tissue, no questionnaires, no medical info, no commercial business
# USE - 7                             yes, no questionnaires, no health treatment, no commercial business
# USE - 8                                          yes, no questionnaires, health treatment when possible
# NOT USE - 9                  yes, no tissue, no questionnaires, no health treatment, no commerical business
# USE - 10                               yes, no health treatment, no medical info, no commercial business
# NOT USE - 11 yes, no tissue, no questionnaires, no health treatment, no medical info, no commercial business
# USE - 12                                                     yes, no questionnaires, no health treatment
# NOT USE - 13                                                             yes, no tissue, no health treatment
# NOT USE - 14                                                               yes, no tissue, no questionnaires
# NOT USE - 15                                                  yes, no tissue, health treatment when possible
# NOT USE - 16                                                                                  yes, no tissue
# USE - 17                                                                     yes, no commerical business
# USE - 18                                     yes, health treatment when possible, no commercial business
# USE - 19                                                    yes, no medical info, no commercial business
# USE - 20                                                                          yes, no questionnaires
# NOT USE - 21                         yes, no tissue, no questionnaires, no health treatment, no medical info
# NOT USE - 22                  yes, no tissue, no questionnaires, no health treatment, no commercial business
# USE - 23                                                                            yes, no medical info
# USE - 24                                                  yes, no questionnaires, no commercial business
# USE - 25                                    yes, no questionnaires, no health treatment, no medical info
# USE - 26                  yes, no questionnaires, health treatment when possible, no commercial business
# USE - 27                                                      yes,  no health treatment, no medical info
# NOT USE - 28                                                                             no, doesn't want to
# NOT USE - 29                                                                              no, unable to sign
# NOT USE - 30                                                                                 no, no reaction
# NOT USE - 31                                                                                        no, lost
# NOT USE - 32                                                                                     no, too old
# NOT USE - 34                                            yes, no medical info, health treatment when possible
# NOT USE - 35                                             no (never asked for IC because there was no tissue)
# USE - 36                    yes, no medical info, no commercial business, health treatment when possible
# NOT USE - 37                                                                                    no, endpoint
# USE - 38                                                         wil niets invullen, wel alles gebruiken
# USE - 39                                           second informed concents: yes, no commercial business
# NOT USE - 40                                                                              nooit geincludeerd

cat("- sanity checking PRIOR to selection")
library(data.table)
require(labelled)
ae.gender <- to_factor(AEDB$Gender)
ae.hospital <- to_factor(AEDB$Hospital)
table(ae.gender, ae.hospital, dnn = c("Sex", "Hospital"))
ae.artery <- to_factor(AEDB$Artery_summary)
table(ae.artery, ae.gender, dnn = c("Sex", "Artery"))

rm(ae.gender, ae.hospital, ae.artery)

# I change numeric and factors manually because, well, I wouldn't know how to fix it otherwise
# to have this 'tibble' work with 'tableone'... :-)

AEDB$Age <- as.numeric(AEDB$Age)
AEDB$diastoli <- as.numeric(AEDB$diastoli)
AEDB$systolic <- as.numeric(AEDB$systolic)

AEDB$TC_finalCU <- as.numeric(AEDB$TC_finalCU)
AEDB$LDL_finalCU <- as.numeric(AEDB$LDL_finalCU)
AEDB$HDL_finalCU <- as.numeric(AEDB$HDL_finalCU)
AEDB$TG_finalCU <- as.numeric(AEDB$TG_finalCU)

AEDB$TC_final <- as.numeric(AEDB$TC_final)
AEDB$LDL_final <- as.numeric(AEDB$LDL_final)
AEDB$HDL_final <- as.numeric(AEDB$HDL_final)
AEDB$TG_final <- as.numeric(AEDB$TG_final)

AEDB$Age <- as.numeric(AEDB$Age)
AEDB$GFR_MDRD <- as.numeric(AEDB$GFR_MDRD)
AEDB$BMI <- as.numeric(AEDB$BMI)
AEDB$eCigarettes <- as.numeric(AEDB$eCigarettes)
AEDB$ePackYearsSmoking <- as.numeric(AEDB$ePackYearsSmoking)
AEDB$EP_composite_time <- as.numeric(AEDB$EP_composite_time)

AEDB$macmean0 <- as.numeric(AEDB$macmean0)
AEDB$smcmean0 <- as.numeric(AEDB$smcmean0)
AEDB$neutrophils <- as.numeric(AEDB$neutrophils)
AEDB$Mast_cells_plaque <- as.numeric(AEDB$Mast_cells_plaque)
AEDB$vessel_density_averaged <- as.numeric(AEDB$vessel_density_averaged)

AEDB$IL6 <- as.numeric(AEDB$IL6)
AEDB$IL6_pg_ug_2015 <- as.numeric(AEDB$IL6_pg_ug_2015)
AEDB$IL6R_pg_ug_2015 <- as.numeric(AEDB$IL6R_pg_ug_2015)
AEDB$MCP1 <- as.numeric(AEDB$MCP1)
AEDB$MCP1_pg_ug_2015 <- as.numeric(AEDB$MCP1_pg_ug_2015)
AEDB$hsCRP_plasma <- as.numeric(AEDB$hsCRP_plasma)

require(labelled)
AEDB$ORyear <- to_factor(AEDB$ORyear)
AEDB$Gender <- to_factor(AEDB$Gender)
AEDB$Hospital <- to_factor(AEDB$Hospital)
AEDB$KDOQI <- to_factor(AEDB$KDOQI)
AEDB$BMI_WHO <- to_factor(AEDB$BMI_WHO)
AEDB$DiabetesStatus <- to_factor(AEDB$DiabetesStatus)
AEDB$SmokerStatus <- to_factor(AEDB$SmokerStatus)
AEDB$AlcoholUse <- to_factor(AEDB$AlcoholUse)

AEDB$Hypertension.selfreport <- to_factor(AEDB$Hypertension1)
AEDB$Hypertension.selfreportdrug <- to_factor(AEDB$Hypertension2)
AEDB$Hypertension.composite <- to_factor(AEDB$Hypertension.composite)
AEDB$Hypertension.drugs <- to_factor(AEDB$Hypertension.drugs)

AEDB$Med.anticoagulants <- to_factor(AEDB$Med.anticoagulants)
AEDB$Med.all.antiplatelet <- to_factor(AEDB$Med.all.antiplatelet)
AEDB$Med.Statin.LLD <- to_factor(AEDB$Med.Statin.LLD)

AEDB$Stroke_Dx <- to_factor(AEDB$Stroke_Dx)
AEDB$CAD_history <- to_factor(AEDB$CAD_history)
AEDB$PAOD <- to_factor(AEDB$PAOD)
AEDB$Peripheral.interv <- to_factor(AEDB$Peripheral.interv)

AEDB$sympt <- to_factor(AEDB$sympt)
AEDB$Symptoms.3g <- to_factor(AEDB$Symptoms.3g)
AEDB$Symptoms.4g <- to_factor(AEDB$Symptoms.4g)
AEDB$Symptoms.5G <- to_factor(AEDB$Symptoms.5G)
AEDB$AsymptSympt <- to_factor(AEDB$AsymptSympt)
AEDB$AsymptSympt2G <- to_factor(AEDB$AsymptSympt2G)


AEDB$restenos <- to_factor(AEDB$restenos)
AEDB$stenose <- to_factor(AEDB$stenose)
AEDB$EP_composite <- to_factor(AEDB$EP_composite)
AEDB$Macrophages.bin <- to_factor(AEDB$Macrophages.bin)
AEDB$SMC.bin <- to_factor(AEDB$SMC.bin)
AEDB$IPH.bin <- to_factor(AEDB$IPH.bin)
AEDB$Calc.bin <- to_factor(AEDB$Calc.bin)
AEDB$Collagen.bin <- to_factor(AEDB$Collagen.bin)
AEDB$Fat.bin_10 <- to_factor(AEDB$Fat.bin_10)
AEDB$Fat.bin_40 <- to_factor(AEDB$Fat.bin_40)
AEDB$OverallPlaquePhenotype <- to_factor(AEDB$OverallPlaquePhenotype)

AEDB$Artery_summary <- to_factor(AEDB$Artery_summary)

AEDB$informedconsent <- to_factor(AEDB$informedconsent)

AEDB.CEA <- subset(AEDB,
                    (Artery_summary == "carotid (left & right)" | Artery_summary == "other carotid arteries (common, external)") & # we only want carotids
                       informedconsent != "missing" & # we are really strict in selecting based on 'informed consent'!
                       informedconsent != "no, died" &
                       informedconsent != "yes, no tissue, no commerical business" &
                       informedconsent != "yes, no tissue, no questionnaires, no medical info, no commercial business" &
                       informedconsent != "yes, no tissue, no questionnaires, no health treatment, no commerical business" &
                       informedconsent != "yes, no tissue, no questionnaires, no health treatment, no medical info, no commercial business" &
                       informedconsent != "yes, no tissue, no health treatment" &
                       informedconsent != "yes, no tissue, no questionnaires" &
                       informedconsent != "yes, no tissue, health treatment when possible" &
                       informedconsent != "yes, no tissue" &
                       informedconsent != "yes, no tissue, no questionnaires, no health treatment, no medical info" &
                       informedconsent != "yes, no tissue, no questionnaires, no health treatment, no commercial business" &
                       informedconsent != "no, doesn't want to" &
                       informedconsent != "no, unable to sign" &
                       informedconsent != "no, no reaction" &
                       informedconsent != "no, lost" &
                       informedconsent != "no, too old" &
                       informedconsent != "yes, no medical info, health treatment when possible" &
                       informedconsent != "no (never asked for IC because there was no tissue)" &
                       informedconsent != "no, endpoint" &
                       informedconsent != "nooit geincludeerd" & 
                     !is.na(AsymptSympt2G))
AEDB.CEA[1:10, 1:10]
dim(AEDB.CEA)
```


```{r Baseline AEDB: creation}
cat("===========================================================================================\n")
cat("CREATE BASELINE TABLE\n")

# Baseline table variables
basetable_vars = c("Hospital", "ORyear",
                   "Age", "Gender", 
                   "TC_finalCU", "LDL_finalCU", "HDL_finalCU", "TG_finalCU", 
                   "TC_final", "LDL_final", "HDL_final", "TG_final", 
                   "hsCRP_plasma",
                   "systolic", "diastoli", "GFR_MDRD", "BMI", 
                   "KDOQI", "BMI_WHO",
                   "SmokerStatus", "AlcoholUse",
                   "DiabetesStatus", 
                   "Hypertension.selfreport", "Hypertension.selfreportdrug", "Hypertension.composite", "Hypertension.drugs", 
                   "Med.anticoagulants", "Med.all.antiplatelet", "Med.Statin.LLD", 
                   "Stroke_Dx", "sympt", "Symptoms.5G", "AsymptSympt", "AsymptSympt2G",
                   "restenos", "stenose",
                   "CAD_history", "PAOD", "Peripheral.interv", 
                   "EP_composite", "EP_composite_time",
                   "macmean0", "smcmean0", "Macrophages.bin", "SMC.bin",
                   "neutrophils", "Mast_cells_plaque",
                   "IPH.bin", "vessel_density_averaged",
                   "Calc.bin", "Collagen.bin", 
                   "Fat.bin_10", "Fat.bin_40", "OverallPlaquePhenotype",
                   "IL6", "IL6_pg_ug_2015", "IL6R_pg_ug_2015",
                   "MCP1", "MCP1_pg_ug_2015")

basetable_bin = c("Gender", 
                  "KDOQI", "BMI_WHO",
                  "SmokerStatus", "AlcoholUse",
                  "DiabetesStatus", 
                  "Hypertension.selfreport", "Hypertension.selfreportdrug", "Hypertension.composite", "Hypertension.drugs", 
                  "Med.anticoagulants", "Med.all.antiplatelet", "Med.Statin.LLD", 
                  "Stroke_Dx", "sympt", "Symptoms.5G", "AsymptSympt", "AsymptSympt2G",
                  "restenos", "stenose",
                  "CAD_history", "PAOD", "Peripheral.interv", 
                  "EP_composite", "Macrophages.bin", "SMC.bin",
                  "IPH.bin", 
                  "Calc.bin", "Collagen.bin", 
                  "Fat.bin_10", "Fat.bin_40", "OverallPlaquePhenotype")
# basetable_bin

basetable_con = basetable_vars[!basetable_vars %in% basetable_bin]
# basetable_con
```

### Athero-Express Biobank Study: baseline characteristics
Showing the baseline table of the whole Athero-Express Biobank.
```{r Baseline AEDB: Visualize}
# Create baseline tables
# http://rstudio-pubs-static.s3.amazonaws.com/13321_da314633db924dc78986a850813a50d5.html
AEDB.CEA.tableOne = print(CreateTableOne(vars = basetable_vars, 
                                         # factorVars = basetable_bin,
                                         strata = "AsymptSympt2G",
                                         data = AEDB.CEA, includeNA = TRUE), 
                          nonnormal = c(), missing = TRUE,
                          quote = FALSE, noSpaces = FALSE, showAllLevels = TRUE, explain = TRUE, 
                          format = "pf", 
                          contDigits = 3)[,1:6]

```


## AESCRNA: baseline characteristics

```{r Baseline: creation}
metadata <- scRNAseqData@meta.data %>% as_tibble()
scRNAseqDataMeta <- metadata %>% distinct(Patient, .keep_all = TRUE)

scRNAseqDataMetaAE <- merge(scRNAseqDataMeta, AEDB, by.x = "Patient", by.y = "STUDY_NUMBER", sort = FALSE, all.x = TRUE)
dim(scRNAseqDataMetaAE)

# Replace missing data 
# Ref: https://cran.r-project.org/web/packages/naniar/vignettes/replace-with-na.html
require(naniar)

na_strings <- c("NA", "N A", "N / A", "N/A", "N/ A", 
                "Not Available", "Not available", 
                "missing", 
                "-999", "-99", 
                "No data available/missing", "No data available/Missing")
# Then you write ~.x %in% na_strings - which reads as “does this value occur in the list of NA strings”.

scRNAseqDataMetaAE %>%
  replace_with_na_all(condition = ~.x %in% na_strings)

cat("====================================================================================================")
cat("SELECTION THE SHIZZLE")

cat("- sanity checking PRIOR to selection")
library(data.table)
require(labelled)
ae.gender <- to_factor(scRNAseqDataMetaAE$Gender)
ae.hospital <- to_factor(scRNAseqDataMetaAE$Hospital)
table(ae.gender, ae.hospital, dnn = c("Sex", "Hospital"), useNA = "ifany")

ae.artery <- to_factor(scRNAseqDataMetaAE$Artery_summary)
table(ae.artery, ae.gender, dnn = c("Sex", "Artery"), useNA = "ifany")

ae.ic <- to_factor(scRNAseqDataMetaAE$informedconsent)
table(ae.ic, ae.gender, useNA = "ifany")

rm(ae.gender, ae.hospital, ae.artery, ae.ic)


scRNAseqDataMetaAE.all <- subset(scRNAseqDataMetaAE,
                            (Artery_summary == "carotid (left & right)" | Artery_summary == "other carotid arteries (common, external)" ) & # we only want carotids
                              informedconsent != "missing" & # we are really strict in selecting based on 'informed consent'!
                              informedconsent != "no, died" &
                              informedconsent != "yes, no tissue, no commerical business" &
                              informedconsent != "yes, no tissue, no questionnaires, no medical info, no commercial business" &
                              informedconsent != "yes, no tissue, no questionnaires, no health treatment, no commerical business" &
                              informedconsent != "yes, no tissue, no questionnaires, no health treatment, no medical info, no commercial business" &
                              informedconsent != "yes, no tissue, no health treatment" &
                              informedconsent != "yes, no tissue, no questionnaires" &
                              informedconsent != "yes, no tissue, health treatment when possible" &
                              informedconsent != "yes, no tissue" &
                              informedconsent != "yes, no tissue, no questionnaires, no health treatment, no medical info" &
                              informedconsent != "yes, no tissue, no questionnaires, no health treatment, no commercial business" &
                              informedconsent != "no, doesn't want to" &
                              informedconsent != "no, unable to sign" &
                              informedconsent != "no, no reaction" &
                              informedconsent != "no, lost" &
                              informedconsent != "no, too old" &
                              informedconsent != "yes, no medical info, health treatment when possible" & 
                              informedconsent != "no (never asked for IC because there was no tissue)" &
                              informedconsent != "no, endpoint" &
                              informedconsent != "nooit geincludeerd")
# scRNAseqDataMetaAE.all[1:10, 1:10]
dim(scRNAseqDataMetaAE.all)
# DT::datatable(scRNAseqDataMetaAE.all)

```

Showing the baseline table.
```{r Baseline: Visualize}
cat("===========================================================================================")
cat("CREATE BASELINE TABLE")

# Create baseline tables
# http://rstudio-pubs-static.s3.amazonaws.com/13321_da314633db924dc78986a850813a50d5.html
scRNAseqDataMetaAE.all.tableOne = print(CreateTableOne(vars = basetable_vars, 
                                                  # factorVars = basetable_bin,
                                                  strata = "AsymptSympt2G", 
                                                  data = scRNAseqDataMetaAE.all, includeNA = TRUE), 
                                   nonnormal = c(), 
                                   quote = FALSE, showAllLevels = TRUE,
                                   format = "p", 
                                   contDigits = 3)[,1:2]

```

Writing the baseline table to Excel format. 
```{r Baseline: write}
# Write basetable
require(openxlsx)
write.xlsx(file = paste0(OUT_loc, "/",Today,".",PROJECTNAME,".AE.BaselineTable.scRNAseq.xlsx"), 
           format(scRNAseqDataMetaAE.all.tableOne, digits = 5, scientific = FALSE) , row.names = TRUE, col.names = TRUE)


```


# AESCRNA

## Quality control
Here review the number of cells per sample, plate, and patients. And plot the ratio's per sample and study number.
```{r QualityControl}
## check stuff
cat("\nHow many cells per type ...?")
sort(table(scRNAseqData@meta.data$SCT_snn_res.0.8))

cat("\n\nHow many cells per plate ...?")
sort(table(scRNAseqData@meta.data$ID))

cat("\n\nHow many cells per type per plate ...?")
table(scRNAseqData@meta.data$SCT_snn_res.0.8, scRNAseqData@meta.data$ID)

cat("\n\nHow many cells per patient ...?")
sort(table(scRNAseqData@meta.data$Patient))

cat("\n\nVisualizing these ratio's per study number and sample ...?")
UMAPPlot(scRNAseqData, label = TRUE, pt.size = 1.25, label.size = 4, group.by = "ident",
         repel = TRUE)
ggsave(paste0(PLOT_loc, "/", Today, ".UMAP.png"), plot = last_plot())
ggsave(paste0(PLOT_loc, "/", Today, ".UMAP.ps"), plot = last_plot())


barplot(prop.table(x = table(scRNAseqData@active.ident, scRNAseqData@meta.data$Patient)), 
        cex.axis = 1.0, cex.names = 0.5, las = 1,
        col = uithof_color, xlab = "study number", legend.text = FALSE, args.legend = list(x = "bottom"))
dev.copy(pdf, paste0(QC_loc, "/", Today, ".cell_ratios_per_sample.pdf"))
dev.off()

barplot(prop.table(x = table(scRNAseqData@active.ident, scRNAseqData@meta.data$ID)), 
        cex.axis = 1.0, cex.names = 0.5, las = 2,
        col = uithof_color, xlab = "sample ID", legend.text = FALSE, args.legend = list(x = "bottom"))
dev.copy(pdf, paste0(QC_loc, "/", Today, ".cell_ratios_per_sample_per_plate.pdf"))
dev.off()



```

## Visualisations

Let's project known cellular markers.

```{r Visualisation: tSNE Exploration}

UMAPPlot(scRNAseqData, label = FALSE, pt.size = 1.25, label.size = 4, group.by = "ident",
         repel = TRUE)

# endothelial cells
FeaturePlot(scRNAseqData, features = c("CD34"), cols =  c("#ECECEC", "#DB003F"))
FeaturePlot(scRNAseqData, features = c("EDN1"), cols =  c("#ECECEC", "#DB003F"))
FeaturePlot(scRNAseqData, features = c("EDNRA", "EDNRB"), cols =  c("#ECECEC", "#DB003F"))
FeaturePlot(scRNAseqData, features = c("CDH5", "PECAM1"), cols =  c("#ECECEC", "#DB003F"))
FeaturePlot(scRNAseqData, features = c("ACKR1"), cols =  c("#ECECEC", "#DB003F"))

# SMC
FeaturePlot(scRNAseqData, features = c("MYH11"), cols =  c("#ECECEC", "#DB003F"))
FeaturePlot(scRNAseqData, features = c("LGALS3", "ACTA2"), cols =  c("#ECECEC", "#DB003F"))

# macrophages
FeaturePlot(scRNAseqData, features = c("CD14", "CD68"), cols =  c("#ECECEC", "#DB003F"))
FeaturePlot(scRNAseqData, features = c("CD36"), cols =  c("#ECECEC", "#DB003F"))

# t-cells
FeaturePlot(scRNAseqData, features = c("CD3E"), cols =  c("#ECECEC", "#DB003F"))
FeaturePlot(scRNAseqData, features = c("CD4"), cols =  c("#ECECEC", "#DB003F"))
# FeaturePlot(scRNAseqData, features = c("CD8"), cols =  c("#ECECEC", "#DB003F"))

# b-cells
FeaturePlot(scRNAseqData, features = c("CD79A"), cols =  c("#ECECEC", "#DB003F"))

# mast cells
FeaturePlot(scRNAseqData, features = c("KIT"), cols =  c("#ECECEC", "#DB003F"))

# NK cells
FeaturePlot(scRNAseqData, features = c("NCAM1"), cols =  c("#ECECEC", "#DB003F"))

```


## Targets of interest: _`r TARGET_GENES`_

We check whether the targets genes, _`r TARGET_GENES`_, were sequenced using our method (STARseq). Given that _COL4A1/2_ are downstream targets of _MMP2_ we also checked the cell type specific expression of these genes.

### Expression in cell communities
```{r Visualisation: Targets A}
UMAPPlot(scRNAseqData, label = FALSE, pt.size = 1.25, label.size = 4, group.by = "ident",
         repel = TRUE)

VlnPlot(scRNAseqData, features = TARGET_A_alt) + 
  xlab("cell communities") + 
  ylab(bquote("normalized "~italic(CCL2)~" expression")) +
  theme(
    axis.title.x = element_text(color = "#000000", size = 14, face = "bold"),
    axis.title.y = element_text(color = "#000000", size = 14, face = "bold"), 
    legend.position = "none")
ggsave(paste0(PLOT_loc, "/", Today, ".VlnPlot.",TARGET_A,".png"), plot = last_plot())
ggsave(paste0(PLOT_loc, "/", Today, ".VlnPlot.",TARGET_A,".ps"), plot = last_plot())

VlnPlot(scRNAseqData, features = TARGET_B) + 
  xlab("cell communities") + 
  ylab(bquote("normalized "~italic(CCR2)~" expression")) +
  theme(
    axis.title.x = element_text(color = "#000000", size = 14, face = "bold"),
    axis.title.y = element_text(color = "#000000", size = 14, face = "bold"), 
    legend.position = "none")
ggsave(paste0(PLOT_loc, "/", Today, ".VlnPlot.",TARGET_B,".png"), plot = last_plot())
ggsave(paste0(PLOT_loc, "/", Today, ".VlnPlot.",TARGET_B,".ps"), plot = last_plot())

# VlnPlot(scRNAseqData, features = TARGET_C) + 
#   xlab("cell communities") + 
#   ylab(bquote("normalized "~italic(IL6)~" expression")) +
#   theme(
#     axis.title.x = element_text(color = "#000000", size = 14, face = "bold"),
#     axis.title.y = element_text(color = "#000000", size = 14, face = "bold"), 
#     legend.position = "none")
# ggsave(paste0(PLOT_loc, "/", Today, ".VlnPlot.",TARGET_C,".png"), plot = last_plot())
# ggsave(paste0(PLOT_loc, "/", Today, ".VlnPlot.",TARGET_C,".ps"), plot = last_plot())
# 
# VlnPlot(scRNAseqData, features = TARGET_D) + 
#   xlab("cell communities") + 
#   ylab(bquote("normalized "~italic(IL6R)~" expression")) +
#   theme(
#     axis.title.x = element_text(color = "#000000", size = 14, face = "bold"),
#     axis.title.y = element_text(color = "#000000", size = 14, face = "bold"), 
#     legend.position = "none")
# ggsave(paste0(PLOT_loc, "/", Today, ".VlnPlot.",TARGET_D,".png"), plot = last_plot())
# ggsave(paste0(PLOT_loc, "/", Today, ".VlnPlot.",TARGET_D,".ps"), plot = last_plot())

# GenesOfInterest_TargetsA <- c("CCL2", "CCR2", "IL6", "IL6R", "ACTA2", "MYH11", "CD34", "CD14", "CD68", "CD3E", "CD4")
# GenesOfInterest_TargetsA <- c("CCL2", "CCR2", "IL6", "IL6R")
GenesOfInterest_TargetsA <- c("CCL2", "CCR2")

library(RColorBrewer)

p1 <- DotPlot(scRNAseqData, features = GenesOfInterest_TargetsA, 
        cols = "RdBu")

p1 + theme(axis.text.x = element_text(angle = 45, hjust=1))

ggsave(paste0(PLOT_loc, "/", Today, ".DotPlot.Targets.png"), plot = last_plot())
ggsave(paste0(PLOT_loc, "/", Today, ".DotPlot.Targets.ps"), plot = last_plot())

rm(p1)

# Visualize co-expression of two features simultaneously

FeaturePlot(scRNAseqData, features = c(TARGET_A_alt, "CD14"), 
            # cols = c("#ECECEC", "#DB003F", "#9A3480","#1290D9"),
            # combine = TRUE, 
            blend = TRUE)
FeaturePlot(scRNAseqData, features = c(TARGET_A_alt, "CD68"), 
            # cols = c("#ECECEC", "#DB003F", "#9A3480","#1290D9"),
            # combine = TRUE, 
            blend = TRUE)

FeaturePlot(scRNAseqData, features = c(TARGET_B, "CD14"), 
            # cols = c("#ECECEC", "#DB003F", "#9A3480","#1290D9"),
            # combine = TRUE, 
            blend = TRUE)
FeaturePlot(scRNAseqData, features = c(TARGET_B, "CD68"), 
            # cols = c("#ECECEC", "#DB003F", "#9A3480","#1290D9"),
            # combine = TRUE, 
            blend = TRUE)

FeaturePlot(scRNAseqData, features = c(TARGET_B, "CD79A"), 
            # cols = c("#ECECEC", "#DB003F", "#9A3480","#1290D9"),
            # combine = TRUE, 
            blend = TRUE)
FeaturePlot(scRNAseqData, features = c(TARGET_B, "CD79A"), 
            # cols = c("#ECECEC", "#DB003F", "#9A3480","#1290D9"),
            # combine = TRUE, 
            blend = TRUE)

# FeaturePlot(scRNAseqData, features = c(TARGET_D, "CD79A"), 
#             # cols = c("#ECECEC", "#DB003F", "#9A3480","#1290D9"),
#             # combine = TRUE, 
#             blend = TRUE)
# FeaturePlot(scRNAseqData, features = c(TARGET_D, "CD79A"), 
#             # cols = c("#ECECEC", "#DB003F", "#9A3480","#1290D9"),
#             # combine = TRUE, 
#             blend = TRUE)

FeaturePlot(scRNAseqData, features = c(GenesOfInterest_TargetsA), 
            cols =  c("#ECECEC", "#DB003F", "#9A3480","#1290D9"), 
            combine = TRUE)

ggsave(paste0(PLOT_loc, "/", Today, ".FeaturePlot.Targets.png"), plot = last_plot())
ggsave(paste0(PLOT_loc, "/", Today, ".FeaturePlot.Targets.ps"), plot = last_plot())


FeaturePlot(scRNAseqData, features = c(TARGET_A_alt), cols =  c("#ECECEC", "#DB003F"))
FeaturePlot(scRNAseqData, features = c(TARGET_B), cols =  c("#ECECEC", "#DB003F"))
# FeaturePlot(scRNAseqData, features = c(TARGET_C), cols =  c("#ECECEC", "#DB003F"))
# FeaturePlot(scRNAseqData, features = c(TARGET_D), cols =  c("#ECECEC", "#DB003F"))

```

### Differential expression between cell communities

#### Macrophages

Comparison between the macrophages cell communities (_CD14/CD68_<sup>+</sup>), and all other communities.
```{r Visualisation: Volcano MAC}
N_GENES=20552
MAC.markers <- FindMarkers(object = scRNAseqData, 
                          ident.1 = c("CD14+CD68+ M I", 
                                      "CD14+CD68+ M II", 
                                      "CD14+CD68+ M III"), 
                          ident.2 = c(#"CD14+CD68+ M I", 
                                      #"CD14+CD68+ M II", 
                                      #"CD14+CD68+ M III",
                                      "CD3+CD8+ T I",
                                      "CD3+CD8A+ T II ", 
                                      "CD3+CD8 T III", 
                                      "CD3+CD4+ T I", 
                                      "CD3+CD4+ T II", 
                                      "CD3+CD4+ T III", 
                                      "CD34+ EC I", "CD34+ EC II",
                                      "Mixed I", 
                                      "Mixed II", 
                                      "ACTA2+ SMC", 
                                      "NCAM1+ NK", 
                                      "KIT+ MC",
                                      "CD79A+ B I", 
                                      "CD79A+ B II"))

DT::datatable(MAC.markers)
MAC_Volcano_TargetsA = EnhancedVolcano(MAC.markers,
    lab = rownames(MAC.markers),
    x = "avg_log2FC",
    y = "p_val_adj",
    selectLab = GenesOfInterest_TargetsA,
    axisLabSize = 12,
    xlab = "average fold-change",
    title = "Macrophage markers\n(Macrophage communities vs the rest)",
    titleLabSize = 14,
    pCutoff = 0.05/N_GENES, # 20552 genes
    FCcutoff = 1.25,
    pointSize = 1.5,
    labSize = 3.0,
    legendLabels = c('NS','avg. fold-change','P',
      'P & avg. fold-change'),
    legendPosition = "right",
    legendLabSize = 10,
    legendIconSize = 3.0,
    drawConnectors = TRUE,
    widthConnectors = 0.2,
    colConnectors = "#595A5C",
    gridlines.major = FALSE,
    gridlines.minor = FALSE)

MAC_Volcano_TargetsA
ggsave(paste0(PLOT_loc, "/", Today, ".Volcano.MAC.DEG.Targets.pdf"), 
       plot = MAC_Volcano_TargetsA)
```

On average _FTL_, _LYZ_, _HLA-DRA_, _IFI30_, and _TCSB_ among others have a higher expression in the macrophage cell communities, than in all the others. 

Below the results for the `TARGET_GENES`.
```{r results MACs}
# temp = subset(MAC.markers, (rownames(MAC.markers)=="CCL2" | rownames(MAC.markers)=="CCR2" | rownames(MAC.markers)=="IL6" | rownames(MAC.markers)=="IL6R"))
temp = subset(MAC.markers, (rownames(MAC.markers)=="CCL2" | rownames(MAC.markers)=="CCR2"))
DT::datatable(temp)
rm(temp)
```

We will save these results too,

```{r SAVE results MACs}

MAC.markers <- data.frame(Gene = rownames(MAC.markers), MAC.markers)
head(MAC.markers)

fwrite(MAC.markers, file = paste0(OUT_loc,"/",Today,".MAC.markers.txt.gz"),
       quote = FALSE, sep = "\t", na ="", dec = ".",
       showProgress = TRUE, verbose = FALSE,
       compress = c("gzip"))
```


# Session information

------

    Version:      v1.0.1
    Last update:  2021-02-09
    Written by:   Sander W. van der Laan (s.w.vanderlaan-2[at]umcutrecht.nl).
    Description:  Script to load single-cell RNA sequencing (scRNAseq) data, and perform quality control (QC), and initial mapping to cells.
    Minimum requirements: R version 3.5.2 (2018-12-20) -- 'Eggshell Igloo', macOS Mojave (10.14.2).
    
    Change log
    * v1.0.1 Update to reviewer comments and changes in EnhancedVolcano.
    * v1.0.0 Initial version

------

```{r eval = TRUE}
sessionInfo()
```

# Saving environment
```{r Saving}
save.image(paste0(PROJECT_loc, "/",Today,".",PROJECTNAME,".scrnaseq_results.RData"))
```

------
<sup>&copy; 1979-2021 Sander W. van der Laan | s.w.vanderlaan-2[at]umcutrecht.nl | [swvanderlaan.github.io](https://swvanderlaan.github.io).</sup>
------

